Senior AI/ML Engineer at Cytel

United States

<p>The Senior AI/ML Engineer will design, develop, and deploy scalable AI-enabled solutions that enhance and automate Statistical Programming workflows. The role will focus on Generative AI, RAG, and agentic solutions, leveraging Python, cloud technologies, and modern software engineering practices to create secure, reliable, and reusable applications. The individual will partner closely with Statistical Programming, IT, Architecture, Security, and Governance teams to move AI solutions from proof of concept into validated, production-ready environments, with a strong emphasis on quality, traceability, reproducibility, and human oversight.</p> <h3>Responsibilities</h3><ul style="list-style-type: disc; padding-left: 72px;"><li><p style="line-height: 120%;"><span>Design and develop AI-enabled solutions for Statistical Programming, contributing to a scalable, modular, secure, and reusable architecture across multiple studies and use cases.</span></p></li><li><p style="line-height: 120%;"><span>Build end-to-end Generative AI and agentic workflows encompassing data and metadata ingestion, retrieval, reasoning, tool use, code generation, execution, validation, and human review.</span></p></li><li><p style="line-height: 120%;"><span>Develop RAG and knowledge-driven solutions that integrate organizational standards, metadata, specifications, historical study assets, programming conventions, and other approved knowledge sources.</span></p></li><li><p style="line-height: 120%;"><span>Develop modular AI services, APIs, and reusable components, appropriately separating deterministic business rules and standards from probabilistic AI/LLM-based reasoning and generation.</span></p></li><li><p style="line-height: 120%;"><span>Implement controls for AI reliability, reproducibility, and quality, including structured inputs/outputs, prompt and model versioning, validation rules, automated evaluation, regression testing, and quality checks of AI-generated artifacts.</span></p></li><li><p style="line-height: 120%;"><span>Build traceability and human-in-the-loop capabilities supporting review, approval, feedback, exception handling, audit trails, and lineage from source information and retrieved context through generated outputs.</span></p></li><li><p style="line-height: 120%;"><span>Support deployment and LLMOps practices across development, testing, validation, and production environments, including Git/CI/CD, monitoring, logging, model and prompt lifecycle management, security, and performance/cost optimization.</span></p></li><li><p style="line-height: 120%;"><span>Collaborate with Statistical Programming, Enterprise Architecture, IT/Cloud, Security, Validation, and Governance teams to transition AI proofs of concept into scalable enterprise solutions while evaluating emerging AI technologies and architectural patterns.</span></p></li><li><p style="line-height: 120%;"><span>Other duties as assigned.</span><br>&nbsp;</p></li></ul> <h3>Qualifications</h3><ul style="list-style-type: disc;"><li style="line-height: 120%;"><span style="color: black;">Bachelor’s or master’s degree in computer science, Engineering, Artificial Intelligence, Data Science</span></li><li style="line-height: 120%;"><span>5+ years of hands-on experience in software engineering, AI/ML engineering, data engineering, or related technical roles, with demonstrated experience building and deploying production-quality applications.</span></li><li style="line-height: 120%;"><span>Hands-on experience developing Generative AI/LLM solutions, with knowledge of RAG, prompt/context engineering, embeddings, vector search, structured outputs, tool/function calling, and agentic AI workflows.</span></li><li style="line-height: 120%;"><span>Strong programming skills in Python and experience with modern software engineering practices, including modular design, APIs, Git, automated testing, CI/CD, and preferably containerized/cloud-based applications.</span></li><li style="line-height: 120%;"><span>Experience working with AWS and familiarity with cloud-based AI/ML services, data storage, security/access controls, logging, and monitoring.</span></li><li style="line-height: 120%;"><span>Understanding of AI reliability and evaluation concepts, including reproducibility, hallucination mitigation, validation, prompt/model versioning, automated evaluation, regression testing, traceability, and human-in-the-loop approaches.</span></li><li style="line-height: 120%;"><span>Strong analytical and problem-solving skills with the ability to work across technical and business teams; experience in pharmaceutical/biotechnology or regulated environments and familiarity with clinical data, Statistical Programming, SAS/R, CDISC/SDTM/ADaM, or GxP principles is preferred but not required.</span></li><li style="line-height: 120%;"><span>Good communication and organizational skills required;</span><br>&nbsp;</li></ul>