Pharma AI/ML Engineer for Statistical Production

Responsibilities

• Design and develop AI-enabled solutions for Statistical Programming, contributing to a scalable, modular, secure, and reusable architecture across multiple studies and use cases.
• 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.
• Develop RAG and knowledge-driven solutions that integrate organizational standards, metadata, specifications, historical study assets, programming conventions, and other approved knowledge sources.
• Develop modular AI services, APIs, and reusable components, appropriately separating deterministic business rules and standards from probabilistic AI/LLM-based reasoning and generation.
• 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.
• 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.
• 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.
• 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.
• Other duties as assigned.

Qualifications

• Bachelor’s or master’s degree in computer science, Engineering, Artificial Intelligence, Data Science
• 2+ 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.
• 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.
• 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.
• Experience working with AWS and familiarity with cloud-based AI/ML services, data storage, security/access controls, logging, and monitoring.
• 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.
• 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.
• Good communication and organizational skills required

Pay

The potential base pay range for this role, when annualized, is $117,500.00 - $327,200.00. The actual base pay offered may vary based on a number of factors including job-related qualifications such as knowledge, skills, education, and experience; location; and/or schedule (full or part-time). Dependent on the position offered, incentive plans, bonuses, and/or other forms of compensation may be offered, in addition to a range of health and welfare and/or other benefits.