Sr AI/ML Full Stack Engineer
Must pass a drug test and background check once offered position*Position OverviewWe are seeking a Senior AI/ML Full Stack Engineer to design, build, evaluate, and deploy production-grade AI/ML and Generative AI solutions. This role combines hands-on AI/ML engineering with full-stack application development, cloud-native deployment, and MLOps/DevOps practices.The engineer will develop end-to-end AI-enabled applications incorporating large language models (LLMs), machine learning, document intelligence, semantic search, APIs, structured and unstructured data, and modern user interfaces.Key ResponsibilitiesDesign, develop, evaluate, and deploy production-grade Generative AI, LLM, and machine learning applications.Build LLM-powered assistants, document intelligence solutions, semantic search capabilities, embedding pipelines, and AI-enabled enterprise applications.Develop document ingestion, processing, chunking, retrieval, and data integration workflows.Implement prompt engineering, query expansion, citation generation, and retrieval-augmented AI capabilities.Develop evaluation frameworks measuring answer relevance, context relevance, faithfulness, hallucination reduction, and citation accuracy.Implement AI safety and Responsible AI controls, including guardrails, content moderation, blocked topics, and escalation logic.Build responsive front-end applications, backend services, RESTful APIs, authentication/authorization, and integrations with AI/ML services.Design and maintain CI/CD pipelines using Azure DevOps for automated builds, testing, security validation, and deployment.Implement automated unit and integration testing, environment configuration, branching strategies, release management, and deployment automation.Containerize and deploy applications using Docker and cloud-native technologies.Establish production monitoring, logging, observability, performance tracking, and troubleshooting for AI models and applications.Apply strong software engineering and MLOps practices including modular architecture, reusable components, version control, code reviews, testing, security, and performance optimization.Collaborate with product owners, business stakeholders, architects, DevOps engineers, security teams, and other technical teams throughout the development lifecycle.Translate business requirements into secure, scalable, measurable, and maintainable technical solutions.Required QualificationsStrong hands-on development experience with Python and SQL.Proven experience building and deploying production-grade Generative AI and LLM applications.Experience with LLMs, prompt engineering, embeddings, semantic search, query expansion, and citation generation.Experience developing, evaluating, deploying, and monitoring ML/NLP models.Understanding of LLM evaluation techniques including relevance, faithfulness, hallucination reduction, and citation accuracy.Experience implementing AI safety guardrails and Responsible AI controls.Strong full-stack development experience, including modern front-end frameworks, backend development, REST APIs, authentication, and application integrations.Experience building CI/CD pipelines with Azure DevOps.Strong knowledge of Git, automated testing, build/deployment pipelines, release management, and environment configuration.Experience with at least one major cloud platform: Azure, GCP, AWS, or OCI.Experience with containerized application development and deployment using Docker.Experience designing and integrating APIs and AI/ML services into enterprise applications.Experience with production monitoring, logging, observability, troubleshooting, and application/model performance tracking.Strong understanding of secure software development, data privacy, and enterprise application development practices.Strong communication and cross-functional collaboration skills.Experience with several of the following technologies is preferred:Azure DevOpsVertex AIAWS BedrockAmazon SageMakerBigQuerySnowflakeOpenSearchCloud RunDockerAirflowTerraformComparable cloud, AI/ML, MLOps, and DevOps technologiesPreferred ExperienceExperience developing AI solutions using healthcare, clinical, claims, life sciences, regulatory, or other complex enterprise data.Experience taking AI/ML solutions from prototype through production deployment and ongoing operational support.Experience working in environments with significant security, privacy, compliance, and data governance requirements.EducationBachelor's degree required in Computer Science, Data Science, Engineering, Biomedical Engineering, Statistics, Applied Mathematics, or a related technical field.Master's degree or higher preferred in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Biomedical Engineering, Engineering, or a related field.