Sr AI/ML Full Stack Engineer

Must pass a drug test and background check once offered position.

About the role

We 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.

Responsibilities

• Design, 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.

Requirements

• Strong 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.

Preferred Skills

Experience with several of the following technologies is preferred:

• Azure DevOps
• Vertex AI
• AWS Bedrock
• Amazon SageMaker
• BigQuery
• Snowflake
• OpenSearch
• Cloud Run
• Docker
• Airflow
• Terraform
• Comparable cloud, AI/ML, MLOps, and DevOps technologies

• Experience 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.

Qualifications

• Bachelor'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.