AI/ML Engineer

Location: Oakland, CA / Alpharetta, GA Position Summary: We are looking for an experienced Expert AI/ML Engineer to support and advance our enterprise AI, machine learning, and data science capabilities within a healthcare environment. This role requires a strong hands-on background in building machine learning models, supporting data science teams, enabling MLOps, and helping operationalize AI use cases from concept to production. The ideal candidate will have practical experience developing ML models, tuning and improving model performance, troubleshooting issues, and mentoring other team members who are building AI/ML solutions. This individual will also help establish best practices, reusable patterns, governance controls, and operational processes for enterprise AI and ML delivery. This role is especially important for a healthcare organization where data quality, privacy, governance, explainability, compliance, and production reliability are critical . Key Responsibiliti esMachine Learning and Data Scien ceDesign, build, train, tune, validate, and deploy machine learning model s.Support use cases such as prediction, classification, forecasting, anomaly detection, natural language processing, document intelligence, member/provider analytics, operational optimization, and risk identificatio n.Perform exploratory data analysis, feature engineering, model selection, model evaluation, and performance tunin g.Review model outputs and recommend improvements for accuracy, precision, recall, stability, fairness, and explainabilit y.Troubleshoot model performance issues, data quality issues, model drift, production failures, and inconsistent prediction s.Partner with data engineers, data scientists, analytics teams, and business stakeholders to ensure models are built on trusted and governed dat a.AI/ML Mentorship and Technical Leadersh ipMentor and guide team members who are building machine learning and AI model s.Provide hands-on support for model design, development, testing, validation, and deploymen t.Conduct technical reviews of model architecture, code, features, evaluation metrics, and production-readines s.Establish reusable standards, templates, checklists, and best practices for AI/ML deliver y.Help upskill internal teams on data science, ML engineering, MLOps, responsible AI, and AI solution desig n.Serve as a trusted advisor to teams implementing AI and ML use case s. MLOps Enablem entDefine and implement MLOps practices across the machine learning lifecyc le.Support experiment tracking, model versioning, model registry, automated testing, CI/CD, deployment automation, and model monitori ng.Establish processes for model promotion from development to test to producti on.Define model monitoring approaches for accuracy, drift, bias, performance, usage, and operational heal th.Support retraining strategies, rollback procedures, alerting, incident response, and production support mode ls.Partner with platform, DevOps, data engineering, security, and governance teams to operationalize AI/ML solutions safely and reliab ly. AI Solutions in Data and Healthcare Analy ticsHelp identify, assess, and design AI use cases across data, analytics, reporting, operations, governance, and automat ion.Provide technical guidance for AI solutions involving GenAI, LLMs, text-to-SQL, semantic search, summarization, document processing, NLP, and predictive analyt ics.Define end-to-end solution approaches, including data requirements, architecture, model strategy, governance controls, deployment approach, and support mo del.Help move AI initiatives from proof of concept to production-grade implementat ion.Ensure AI solutions are designed with healthcare data privacy, security, explainability, auditability, and responsible AI principles in m ind.Governance, Compliance and Production Readi nessSupport AI/ML governance processes including model documentation, approval workflows, risk assessment, validation, and auditabil ity.Ensure solutions follow enterprise standards for data security, privacy, access control, and regulatory expectati ons.Partner with security, compliance, legal, privacy, architecture, and data governance teams as nee ded.Define production-readiness criteria for AI/ML soluti ons.Support responsible AI practices including bias review, explainability, transparency, human-in-the-loop controls, and monitor ing.Required Skil ls: 8+ years of experience in machine learning, data science, AI engineering, ML engineering, or related ro les.Strong hands-on experience building, tuning, validating, and deploying ML mod els.Experience mentoring data scientists, ML engineers, data engineers, or analytics te ams.Strong knowledge of supervised learning, unsupervised learning, classification, regression, forecasting, NLP, and model evaluation techniq ues.Experience with Python and common ML/data science libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, PyTorch, or simi lar.Practical experience with MLOps concepts such as model registry, experiment tracking, CI/CD, deployment pipelines, monitoring, drift detection, and retrain ing.Experience working with enterprise data platforms, cloud platforms, and modern data engineering practi ces.Strong understanding of data quality, feature engineering, model validation, and production supp ort.Ability to translate business problems into AI/ML solution desi gns.Strong communication skills with the ability to explain technical concepts to both technical and non-technical stakehold ers.Technical Sk illsProgramming: Python, SQLMachine Learning: scikit-learn, XGBoost, TensorFlow, PyTorch, statistical modeling, forecasting, NLPMLOps: MLflow, Azure ML, Dataiku, model registry, CI/CD, GitHub Act ionsData Platforms: Snowflake, Azure SQL, Oracle, data lakes, cloud data platf ormsAI/GenAI: LLMs, prompt engineering, RAG, semantic search, text-to-SQL, document intellig enceGovernance: model documentation, lineage, metadata, data quality, responsible AI, privacy and security cont rolsDesired Skil ls: Experience in healthcare, dental insurance, health insurance, financial services, or another regulated indus try.Experience with platforms such as Azure ML, Dataiku, Databricks, Snowflake, MLflow, GitHub, GitHub Actions, Power BI, or similar to ols.Experience with GenAI and LLM-based soluti ons.Experience designing AI solutions using enterprise data platforms such as Snowflake or cloud-based data ecosyst ems.Experience with responsible AI, model governance, bias detection, explainability, and audit requireme nts.Experience supporting AI governance councils, architecture reviews, or model risk review proces ses.Experience with healthcare data domains such as members, providers, claims, benefits, eligibility, call center, clinical, dental, or operational d ata. 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