Machine Learning Engineer

Machine Learning Engineer Pay Rate: $75–$89/hour Position Summary We are seeking a skilled Machine Learning Engineer (MLOps) to support the full lifecycle of machine learning models, including design, development, deployment, and maintenance. This role focuses on building scalable, production-ready AI/ML solutions and ensuring seamless integration within existing systems. The ideal candidate will collaborate with cross-functional teams to deploy, monitor, and optimize machine learning models that drive operational efficiency, innovation, and data-driven decision-making. This position requires strong experience in MLOps, DevOps practices, and cloud-based AI infrastructure. Key Responsibilities Design, build, deploy, and maintain machine learning models in production environments Develop and manage end-to-end MLOps pipelines, including model versioning, monitoring, and automation Implement scalable ML infrastructure using cloud platforms (AWS, Azure, or GCP) Build and optimize CI/CD pipelines for automated testing and deployment of ML models Collaborate with data scientists, data engineers, and DevOps teams to operationalize AI solutions Monitor model performance, system health, and data drift; implement logging and alerting solutions Ensure reliability, scalability, and performance of ML systems in real-time inference environments Maintain version control for models and code to support reproducibility and collaboration Apply best practices for testing, debugging, and performance optimization Ensure compliance with data security, privacy, and regulatory standards Create and maintain technical documentation for ML systems and processes Required Qualifications Bachelor’s degree in Computer Science, Engineering, Artificial Intelligence, or a related field 3+ years of experience in machine learning engineering or MLOps Hands-on experience managing the end-to-end machine learning lifecycle Strong programming skills in Python, R, and/or SQL Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform Experience with containerization (Docker) and orchestration tools (Kubernetes) Experience with infrastructure as code tools such as Terraform Experience building and maintaining CI/CD pipelines (e.g., GitHub Actions) Strong understanding of software development, system architecture, and deployment processes Experience with monitoring, logging, and performance tuning of ML systems Knowledge of version control systems (e.g., Git) Preferred Qualifications Master’s degree in Computer Science, Engineering, or a related field Experience working with healthcare data or regulated environments Familiarity with Electronic Health Record (EHR) systems Experience with predictive modeling, natural language processing (NLP), and large language models (LLMs) Knowledge of retrieval-augmented generation (RAG) frameworks and their applications Understanding of agile methodologies and DevOps lifecycle practices Core Competencies Production-grade ML model deployment and lifecycle management Scalable infrastructure design for AI/ML workloads Cross-functional collaboration and technical leadership Strong analytical and problem-solving skills Effective technical communication and documentation Show more Show less