21 hours ago
Remote, United StatesSenior
Responsibilities
- Design and develop scalable, modular, secure, and reusable AI-enabled solutions for Statistical Programming.
- Build end-to-end Generative AI and agentic workflows covering ingestion, retrieval, reasoning, tool use, code generation, execution, validation, and human review.
- Develop RAG and knowledge-driven solutions using organizational standards, metadata, specifications, historical study assets, and approved knowledge sources.
- Build modular AI services, APIs, and reusable components while separating deterministic business rules from probabilistic AI/LLM reasoning.
- Implement reliability, reproducibility, quality, traceability, lineage, automated evaluation, regression testing, audit trails, and human-in-the-loop controls.
- Support LLMOps across development, testing, validation, and production, including monitoring, logging, model and prompt lifecycle management, security, and performance/cost optimization.
- Collaborate with technical, Statistical Programming, Security, Validation, and Governance teams to move AI proofs of concept into enterprise production environments.
- Evaluate emerging AI technologies and architectural patterns and perform other duties as assigned.
Requirements
- Bachelor’s or master’s degree in computer science, Engineering, Artificial Intelligence, or Data Science.
- At least 5 years of hands-on experience in software engineering, AI/ML engineering, data engineering, or related technical roles.
- Demonstrated experience building and deploying production-quality applications.
- Hands-on experience developing Generative AI and LLM solutions, including RAG, prompt/context engineering, embeddings, vector search, structured outputs, tool/function calling, and agentic AI workflows.
- Strong Python programming skills and experience with modular design, APIs, Git, automated testing, CI/CD, and preferably containerized or cloud-based applications.
- Experience with AWS and familiarity with cloud-based AI/ML services, data storage, security and access controls, logging, and monitoring.
- Understanding of AI reliability and evaluation concepts, including reproducibility, hallucination mitigation, validation, prompt and model versioning, automated evaluation, regression testing, traceability, and human-in-the-loop approaches.
- Strong analytical, problem-solving, communication, and organizational 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 are preferred but not required.
Categories
About Cytel
Cytel builds clinical-trial design and analytics software and provides biometrics and data-science CRO services for biopharma and medical researchers. Its offerings include adaptive design tools like the East platform, along with statistical programming, data management, and regulatory support delivered via sponsor-dedicated teams. Founded in 1987 and headquartered in Cambridge, MA, Cytel is privately held and backed by Nordic Capital, with a global client base across pharmaceutical and biotechnology markets.