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About The Team Come help us build the world's most reliable on-demand, logistics engine for delivery! We're bringing on talented engineers to help us create and maintain a 24x7, no downtime, global infrastructure system that powers DoorDash's three-sided marketplace of consumers, merchants, and dashers. About The Role At DoorDash, our Data Scientists and ML Engineers have the opportunity to dive into a wealth of delivery data to improve company-wide ML workflows such as Search & Recommendations, Dasher Assignment, ETA Prediction, and Dasher Capacity Planning. You will join a small team to build systems that empower efficient machine learning at scale. This is a hybrid opportunity in San Francisco, Sunnyvale or Seattle. You're excited about this opportunity because you will… Build a world-class ML platform where models are developed, trained, and deployed seamlessly Work closely with Data Scientists and Product Engineers to evolve the ML platform as per their use cases You will help build high performance and flexible pipelines that can rapidly evolve to handle new technologies, techniques and modeling approaches You will work on infrastructure designs and solutions to store trillions of feature values and power hundreds of billions of predictions a day You will help design and drive directions for the centralized machine learning platform that powers all of DoorDash's business. Improve the reliability, scalability, and observability of our training and inference infrastructure. We're Excited About You Because… B.S., M.S., or PhD. in Computer Science or equivalent Exceptionally strong knowledge of CS fundamental concepts and OOP languages 2+ years of industry experience in software engineering Prior experience building machine learning systems in production such as enabling data analytics at scale Prior experience in machine learning - you've developed and deployed your own models - even if these are simple proof of concepts Systems Engineering - you've built meaningful pieces of infrastructure in a cloud computing environment. Bonus if those were data processing systems or distributed systems Nice To Haves Experience with challenges in real-time computing Experience with large scale distributed systems, data processing pipelines and machine learning training and serving infrastructure Familiar with Pandas and Python machine learning libraries and deep learning frameworks such as PyTorch and TensorFlow Familiar with Spark, MLLib, Databricks,MLFlow, Apache Airflow, Dagster and similar related technologies. Familiar with large language models like GPT, LLAMA, BERT, or Transformer-based architectures Familiar with a cloud based environment such as AWS Compensation The successful candidate's starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee's work location. Ranges are market-dependent and may be modified in the future. In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information. DoorDash cares about you and your overall well-being. That's why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others. To learn more about our benefits, visit our careers page here. See Below For Paid Time Off Details For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year. For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week). The national base pay range for this position within the United States, including Illinois and Colorado. $130,600—$192,000 USD About DoorDash At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods. DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees' happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more. Our Commitment to Diversity and Inclusion We're committed to growing and empowering a more inclusive community within our company, industry, and cities. That's why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel. Statement of Non-Discrimination: In keeping with our beliefs and goals, no employee or applicant will face discrimination or harassment based on: race, color, ancestry, national origin, religion, age, gender, marital/domestic partner status, sexual orientation, gender identity or expression, disability status, or veteran status. Above and beyond discrimination and harassment based on "protected categories," we also strive to prevent other subtler forms of inappropriate behavior (i.e., stereotyping) from ever gaining a foothold in our office. Whether blatant or hidden, barriers to success have no place at DoorDash. We value a diverse workforce – people who identify as women, non-binary or gender non-conforming, LGBTQIA+, American Indian or Native Alaskan, Black or African American, Hispanic or Latinx, Native Hawaiian or Other Pacific Islander, differently-abled, caretakers and parents, and veterans are strongly encouraged to apply. Thank you to the Level Playing Field Institute for this statement of non-discrimination. Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation. If you need any accommodations, please inform your recruiting contact upon initial connection. Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only We used Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provided Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023. We resumed using Covey Scout for Inbound again on June 29, 2024, and ceased using Covey Scout for Inbound on April 30, 2026. The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: https://getcovey.com/nyc-local-law-144. Beware of recruitment scams: DoorDash, Deliveroo, and Wolt will never ask you to pay money or share sensitive financial information during hiring — learn more about our legitimate recruiting process at Recruitment Scam Awareness - DoorDash .

About The Team Come help us build the world's most reliable on-demand, logistics engine for delivery! We're bringing on talented engineers to help us create and maintain a 24x7, no downtime, global infrastructure system that powers DoorDash's three-sided marketplace of consumers, merchants, and dashers. About The Role At DoorDash, our Data Scientists and ML Engineers have the opportunity to dive into a wealth of delivery data to improve company-wide ML workflows such as Search & Recommendations, Dasher Assignment, ETA Prediction, and Dasher Capacity Planning. You will join a small team to build systems that empower efficient machine learning at scale. This is a hybrid opportunity in San Francisco, Sunnyvale or Seattle. You're excited about this opportunity because you will… Build a world-class ML platform where models are developed, trained, and deployed seamlessly Work closely with Data Scientists and Product Engineers to evolve the ML platform as per their use cases You will help build high performance and flexible pipelines that can rapidly evolve to handle new technologies, techniques and modeling approaches You will work on infrastructure designs and solutions to store trillions of feature values and power hundreds of billions of predictions a day You will help design and drive directions for the centralized machine learning platform that powers all of DoorDash's business. Improve the reliability, scalability, and observability of our training and inference infrastructure. We're Excited About You Because… B.S., M.S., or PhD. in Computer Science or equivalent Exceptionally strong knowledge of CS fundamental concepts and OOP languages 2+ years of industry experience in software engineering Prior experience building machine learning systems in production such as enabling data analytics at scale Prior experience in machine learning - you've developed and deployed your own models - even if these are simple proof of concepts Systems Engineering - you've built meaningful pieces of infrastructure in a cloud computing environment. Bonus if those were data processing systems or distributed systems Nice To Haves Experience with challenges in real-time computing Experience with large scale distributed systems, data processing pipelines and machine learning training and serving infrastructure Familiar with Pandas and Python machine learning libraries and deep learning frameworks such as PyTorch and TensorFlow Familiar with Spark, MLLib, Databricks,MLFlow, Apache Airflow, Dagster and similar related technologies. Familiar with large language models like GPT, LLAMA, BERT, or Transformer-based architectures Familiar with a cloud based environment such as AWS Compensation The successful candidate's starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee's work location. Ranges are market-dependent and may be modified in the future. In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information. DoorDash cares about you and your overall well-being. That's why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others. To learn more about our benefits, visit our careers page here. See Below For Paid Time Off Details For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year. For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week). The national base pay range for this position within the United States, including Illinois and Colorado. $130,600—$192,000 USD About DoorDash At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods. DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees' happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more. Our Commitment to Diversity and Inclusion We're committed to growing and empowering a more inclusive community within our company, industry, and cities. That's why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel. Statement of Non-Discrimination: In keeping with our beliefs and goals, no employee or applicant will face discrimination or harassment based on: race, color, ancestry, national origin, religion, age, gender, marital/domestic partner status, sexual orientation, gender identity or expression, disability status, or veteran status. Above and beyond discrimination and harassment based on "protected categories," we also strive to prevent other subtler forms of inappropriate behavior (i.e., stereotyping) from ever gaining a foothold in our office. Whether blatant or hidden, barriers to success have no place at DoorDash. We value a diverse workforce – people who identify as women, non-binary or gender non-conforming, LGBTQIA+, American Indian or Native Alaskan, Black or African American, Hispanic or Latinx, Native Hawaiian or Other Pacific Islander, differently-abled, caretakers and parents, and veterans are strongly encouraged to apply. Thank you to the Level Playing Field Institute for this statement of non-discrimination. Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation. If you need any accommodations, please inform your recruiting contact upon initial connection. Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only We used Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provided Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023. We resumed using Covey Scout for Inbound again on June 29, 2024, and ceased using Covey Scout for Inbound on April 30, 2026. The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: https://getcovey.com/nyc-local-law-144. Beware of recruitment scams: DoorDash, Deliveroo, and Wolt will never ask you to pay money or share sensitive financial information during hiring — learn more about our legitimate recruiting process at Recruitment Scam Awareness - DoorDash .

Machine Learning Operations Engineer How this Role Makes an Impact The Imagine team is a growing company, and we are inviting a motivated Machine Learning Operations Engineer to join our talented team! This candidate will play a critical role in bringing machine learning models into reliable, scalable production environments that support innovative healthcare solutions. The ideal candidate will combine strong technical expertise with a solutions-oriented mindset. If you are looking for a place that offers a challenging and fast-paced environment with the opportunity to grow and develop, look no further! We are always looking for quality people to join our growing team. Must-haves include the ability to adapt to an ever-changing environment, work quickly and efficiently, continuously challenge the status quo, and be an innovative and solutions-based thinker. Essential Duties MLOps Strategy & Architecture Work with senior leadership to define and lead the MLOps vision, architecture, and roadmap across model development and production environments Design end-to-end ML pipelines, from data ingestion and feature engineering to training, validation, deployment, and monitoring Establish scalable patterns for model lifecycle management (versioning, retraining, rollback, auditability) Model Development Enablement Partner with Data Science teams to productionize models efficiently and reliably Build and maintain tooling for experimentation, model tracking, and reproducibility (e.g., feature stores, experiment tracking systems) Optimize training workflows for performance, cost, and scalability Production Infrastructure & Deployment Architect and manage cloud-native ML infrastructure in AWS (e.g., SageMaker, EKS, Lambda, S3, Step Functions) Implement CI/CD pipelines for ML workflows, including automated testing, validation, and deployment Enable real-time and batch inference systems with high availability and low latency Monitoring, Reliability & Governance Establish monitoring for model performance, data drift, and system health Implement alerting, logging, and observability frameworks Ensure compliance with security, privacy, and governance standards Other duties as assigned Education and/or Experience Needed BS in computer science, engineering, data science, or related technical field 5+ years of experience in machine learning engineering Qualifications You Must Have 3+ years of hands-on experience in MLOps or production ML systems Deep expertise in AWS cloud services (e.g., SageMaker, EKS, EC2, S3, IAM, CloudWatch) Strong programming skills in Python (and ideally one additional language such as Java) Experience building and maintaining ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, Step Functions) Strong understanding of CI/CD practices and DevOps principles Experience with feature stores, model registries, and experiment tracking tools (e.g., MLflow, Feast) Familiarity with streaming/data processing frameworks (e.g., Kafka, Spark) Experience supporting both real-time and batch ML systems Knowledge of model governance, fairness, and explainability frameworks Employment Type Full Time, Exempt Reporting Structure Sr. Vice President, Data Science Location & Work Authorization Charlotte, NC is the preferred location for this position. This position is not eligible for H-1B visa sponsorship or C2C (Corp-to-Corp) arrangements. Candidates must be authorized to work in the United States without employer sponsorship, now or in the future. At ImagineSoftware, we have a role to play in contributing to an inclusive world. We work every day to lead with our values and beliefs that enable you to develop your potential and bring your full self to the workplace. Our culture of diversity and inclusion enables more creative thinking and better ideas for addressing a more diverse market. We hire driven people from all backgrounds because it makes us a great company, and because it's the right thing to do. If you share these values, you will find a home at ImagineSoftware.

About The Role The role focuses on the infrastructure layer that makes ML possible at scale: CI/CD pipelines for models, reproducible training environments, feature stores, and serving infrastructure that stays up under load. This is not a role for someone who dabbles in DevOps — it is core platform engineering for machine learning systems. The team ships models to production weekly, not quarterly. This role is why that cadence is possible: building the automated pipelines, monitoring, and infrastructure that let ML engineers and data scientists move fast without breaking production inference. Key Responsibilities Build and maintain CI/CD pipelines for ML workflows using GitHub Actions, GitLab CI, or Argo Workflows, including automated testing and validation gates for models and data Design and operate model serving infrastructure using Kubernetes, KServe, Triton Inference Server, or cloud-native serving (SageMaker Endpoints, Vertex AI Endpoints) Implement end-to-end ML pipeline orchestration with tools like Airflow, Kubeflow, or MLflow, covering data ingestion, training, evaluation, and deployment stages Stand up model monitoring and observability: latency metrics, GPU utilization, data drift detection, and automated alerting with Prometheus, Grafana, and custom dashboards Manage infrastructure as code for ML platforms using Terraform and Helm, spanning GPU clusters, storage, and networking across cloud environments Optimize training and inference costs — batch inference strategies, model quantization, autoscaling policies, and spot-instance training jobs Partner with ML engineers and data scientists to productionize experiments, turning notebooks into tested, versioned, deployable pipelines What We Are Looking For 3–6 years of experience in MLOps, ML engineering, or platform/DevOps engineering with a production ML focus Strong Kubernetes experience in production: deployments, autoscaling (HPA/KEDA), GPU scheduling, and debugging distributed workloads Hands-on experience with at least one workflow orchestrator (Airflow, Kubeflow, Argo, Prefect) and experiment tracking (MLflow, W&B) Proficient in Python and/or Go; comfortable writing tooling, not just configuring it Deep familiarity with cloud ML infrastructure on AWS, GCP, or Azure — including cost management and multi-environment setups Solid grounding in ML fundamentals sufficient to reason about training jobs, model versioning, and deployment tradeoffs; BS in CS, engineering, or equivalent practical experience Bonus: Experience with GPU optimization (CUDA profiling, Triton/TensorRT), feature stores (Feast, Tecton), model registries and governance, or LLM serving stacks (vLLM, TGI)

About The Role The role sits at the intersection of machine learning and infrastructure: making sure models that data scientists build actually ship, scale, and stay healthy in production. You will build and own CI/CD pipelines, serving infrastructure, and observability systems for ML workloads — the backbone that turns experiments into reliable, monitored products. Key Responsibilities Design and maintain end-to-end ML pipelines (training, validation, deployment) using tools like Kubeflow, MLflow, Airflow, or Vertex AI Pipelines Build CI/CD workflows for model and code releases using GitHub Actions, GitLab CI, or Jenkins, with automated testing and staged rollout strategies Deploy and scale model serving infrastructure using Kubernetes, Docker, and frameworks like KServe, Seldon, or Triton Inference Server Implement model monitoring for data drift, latency, and performance regression using tools like Evidently, Prometheus, and Grafana, with automated alerting and rollback triggers Manage feature stores and data versioning systems (Feast, DVC, Delta Lake) to ensure consistency between training and serving environments Optimize infrastructure costs and GPU utilization across training and inference workloads Partner with data scientists and ML engineers to productionize new models, providing clear feedback loops on what breaks and why What We Are Looking For 3–7 years of experience in DevOps, platform engineering, or MLOps, with at least 2 years supporting ML systems in production Strong hands-on experience with Kubernetes and Docker, including deploying stateful and GPU-backed workloads Proficiency in Python and Bash; ability to build tooling and automate operational workflows, not just maintain them Experience with at least one major cloud platform (AWS, GCP, or Azure) and its ML/managed services Working knowledge of ML fundamentals — enough to reason about model versioning, evaluation, and drift without needing a data scientist translate Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

LLM Engineer — Entry Level Job Description We are seeking a Generative AI Engineer to build AI-powered applications using large language models and generative AI technologies. Responsibilities Develop GenAI applications and chatbots Work with LLM APIs Implement prompt engineering techniques Build retrieval-augmented generation (RAG) systems Requirements Strong Python programming skills Knowledge of LLMs and generative AI concepts Familiarity with OpenAI API and LangChain Understanding of vector databases

AI Prompt Designer – Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Job Title: AI Prompt Designer Location: 1 00% Remote (U.S.) Position Type: Full-time, Direct W2 Salary Range: $100,000 Annually Experience Required: 6+ years Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position. Job Summary We are looking for a AI Prompt Designer to define and lead the strategy, patterns, and tooling for designing prompts, agentic workflows, and LLM-based application architectures across the organization. The role combines deep practical mastery of modern LLMs with the discipline of building reusable design patterns, evaluation frameworks, and developer tooling that scale across many teams and use cases. The ideal candidate has shipped LLM-powered products in production, is fluent in prompt design patterns and agent architectures, and brings strong judgment about when and how to apply different LLM techniques responsibly. Required Qualifications Bachelor's or Master's degree in Computer Science, Computational Linguistics, or a related field Six or more years of software engineering experience, with significant time on LLM-based applications Demonstrated experience shipping LLM-powered products to production Deep familiarity with modern LLM APIs and agent frameworks Strong understanding of retrieval-augmented generation, embeddings, and vector databases Experience designing evaluation pipelines for non-deterministic systems Strong Python skills and comfort with modern application frameworks Solid grasp of responsible AI principles, including safety and policy considerations Excellent written and verbal communication skills Track record of mentoring engineers and influencing technical direction Preferred Qualifications Public writing, talks, or open-source contributions on LLM application development Experience with multi-agent architectures and complex tool-use systems Familiarity with fine-tuning workflows and when to choose them over prompting Exposure to product domains such as customer support, coding assistants, or analytics agents Experience integrating LLMs into enterprise software systems with strict compliance requirements How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to hilda@bvteck.com Bright Vision Technologies is an Equal Opportunity Employer. Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment. Powered by JazzHR PxKrAbSLZD

Role : Prompt Engineer Location : Irving, TX (Hybrid) Job Role : Full Time Job Description- A prompt engineer designs, tests, and optimizes instructions (prompts) for large language models (LLMs) to ensure the AI generates accurate, safe, and useful outputs. Role Overview Primary Goal : Bridge the gap between human business needs and artificial intelligence capabilities. Core Function : Craft system instructions, few-shot examples, and context guidelines, so AI tools produce reliable responses instead of hallucinations (false or made-up information). Key Responsibilities Design & Optimization: Write and refine prompts for chatbots, virtual copilots, and retrieval-augmented generation (RAG) systems. Support context engineering, prompt engineering, and RAG workflows, including chunking, embeddings, semantic search, and knowledge graphs. Help build agentic workflows and AI agents with frameworks such as Google ADK, LangGraph, or CrewAI. Connect agents with external tools/data via MCP; A2A exposure is a plus. Assist with deployment, monitoring, data preprocessing, API development, and code reviews Testing & Evaluation: Test model outputs against accuracy, safety, latency (response delay), and token cost metrics. Library Management: Build and maintain reusable prompt templates and governance standards for enterprise teams. Adversarial Testing: Run stress tests to check for prompt injections (tricking the AI into breaking rules), data leaks, and bias. Cross-Functional Collaboration: Partner with data scientists, software developers, product managers, and business stakeholders. Common Qualifications & Skills AI & LLM Familiarity: Understanding of tokenization, context windows, and APIs (such as OpenAI, Anthropic, or Azure OpenAI). Python for GenAI, data preprocessing, and scripting. Core GenAI concepts: foundation models, LLMs, tokenization, embeddings, and context windows. Hands-on prompt/context engineering and practical RAG experience. Working knowledge of knowledge graphs and Graph RAG. Exposure to agentic AI, tool-using agents, multi-step workflows, and frameworks such as ADK, LangGraph, CrewAI, or the OpenAI Agents SDK. Awareness of tool/function calling, MCP, A2A, agent harness, memory, and guardrails. Experience with GenAI APIs such as OpenAI, Gemini, and Claude; orchestration frameworks such as LangChain or LlamaIndex; Docker, Git, and AI compliance/privacy principles. Communication & Linguistics: Strong command of syntax, writing, and clear logical phrasing. Technical Skills: Varying by role—some positions focus purely on writing and psychology, while technical roles require basic Python, SQL, or software workflow knowledge.

Job Description Job Description Position Title: ECA Reports to: Centre Director Vacancies: 1 Location: 30 Denton Ave Toronto, Ontario M1L4P2 Compensation Package: We offer a comprehensive compensation package: Competitive hourly rate starting at $20.58 $2.00 stipend Health Benefits (medical and dental). Wellness and Vacation Days. Focused Training and Development – CFEL focuses on creating a fun and creative environment to inspire our educators to grow and excel. We go above and beyond the ave…

Store Manager Here at Iceland and The Food Warehouse, we pride ourselves on being an innovative, fast-paced, family-orientated Retailer. We are the fastest growing multi-channel Retailer in the UK and as one of the Sunday Times Best Places to Work , we believe that there is no better place to excel in your career as a Store Manager. We work hard to deliver industry leading service to our customers and we are deeply passionate about our products and store offering. We have immense pride in our s…

Shop Manager Location: Ringwood Salary: £25,458 per annum, pro rata Hours: 28 per week Contract: Permanent Are you ready to lead the team representing the British Red Cross in your community? We're in search of an enthusiastic Shop Manager to lead our friendly charity shop. If you're a retail expert with a knack for motivating a team, this role offers a chance to shine! Come join our journey to spread kindness. "Nothing beats the feeling of the team pulling it out of the bag to excel. Generatin…

Applied AI Engineer United States, hybrid or remote. Competitive salary plus benefits. $150,000 to $175,000 + 15% Bonus This is an opportunity to take ownership of real, production-grade Generative AI solutions in a highly applied environment. You will work on end-to-end AI delivery, from early prototypes through to scalable deployment, with clear visibility of the impact your work has on customers and internal teams. The Company They are a well-established organisation operating in a highly regulated, customer-focused industry. Data and AI are central to their strategy, with significant investment in modern platforms and applied AI capabilities. The business values practical innovation, collaboration, and building technology that delivers measurable outcomes at scale. The Role You will be a senior, hands-on contributor within an Applied AI team, responsible for turning GenAI concepts into robust solutions. Key responsibilities include: Designing, building, and deploying Generative AI applications across client-facing and internal platforms. Developing LLM-powered solutions using prompt engineering, vector databases, and Retrieval-Augmented Generation pipelines. Building and owning AI chatbots and conversational agents to production standards, including performance, scalability, and compliance. Creating AI agents across digital and voice channels to support real-time interactions. Delivering proof-of-concept solutions for use cases such as document understanding, summarisation, and knowledge assistants. Optimising model usage, prompts, and parameters to balance accuracy, cost, and reliability. Collaborating with product, engineering, data, and external partners to align AI solutions with business priorities. Your Skills and Experience You will bring strong, practical experience applying Generative AI in commercial settings, including: Advanced Python development skills with modern GenAI and machine learning libraries. Hands-on experience with large language models, prompt engineering, and LLM evaluation. Proven capability building RAG pipelines and working with vector databases. Strong SQL skills and experience working with cloud data platforms. Experience delivering production-grade conversational AI or chatbot solutions. The ability to communicate complex AI concepts clearly to technical and non-technical stakeholders. Comfort working in fast-paced, cross-functional environments. What They Offer Competitive salary and comprehensive benefits package. The chance to work on high-impact, enterprise-scale AI products. Exposure to a wide range of GenAI use cases, including conversational AI and intelligent automation. Clear opportunities for technical growth and influence within a growing Data and AI function. A collaborative, supportive culture that values continuous improvement and learning. How to Apply If you are an Applied AI Engineer looking to build and deploy meaningful Generative AI solutions, apply now to find out more. Desired Skills and Experience AI