Senior AI​/ML Engineer, Production AI; Contractor

Senior AI/ML Engineer, Production AI (Contractor)
Role Overview

• Develop, design, deploy, monitor, and govern enterprise‑ready ML and Generative AI systems that are scalable, auditable, and compliant with internal AI policies and regulatory expectations.
• Help establish MLOps and GenAI Ops foundations, including evaluation, observability, and Responsible AI controls.

• AI/ML & GenAI Engineering
  • Design, build, and deploy production‑grade ML and Generative AI solutions from prototypes to hardened services.
  • Implement GenAI patterns: RAG; prompt engineering and prompt versioning; embedding pipelines and vector search; secure API‑based model access.
  • Ensure AI systems meet enterprise standards for scalability, performance, reliability, and security.
• MLOps & GenAI Ops Frameworks
  • Build/configure end‑to‑end MLOps and GenAI Ops frameworks: model and prompt versioning; reproducible pipelines and CI/CD; controlled deployment and rollback strategies.
  • Integrate AI workflows with enterprise data platforms, orchestration tools, and cloud infrastructure.
• Model & GenAI Evaluation
  • Define evaluation frameworks for ML and GenAI (accuracy/robustness/drift; LLM response quality, grounding, hallucination risk, safety checks; bias/fairness/explainability).
  • Establish acceptance criteria and validation artifacts for regulated, audit‑ready environments.
• Observability & Monitoring
  • Implement observability to monitor ML/LLM degradation, data/embedding drift, prompt/response behavior, latency/failure modes/usage patterns.
  • Enable full logging and traceability for investigations, audits, and continuous improvement.
• Responsible & Ethical AI
  • Apply Responsible AI principles (human‑in‑the‑loop controls; transparency/explainability/proper‑use disclosures; privacy/access control/lineage).
  • Ensure GenAI features are opt‑in, governed, and aligned with AI policies and regulatory expectations.
• Collaboration & Leadership
  • Partner with Data Engineering, Architecture, Security, QA, and Business teams.
  • Translate business problems into well‑scoped, governed AI/GenAI solutions.
  • Contribute to enterprise AI standards, reference architectures, and platform roadmaps.

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
• 5+ years deploying ML systems in production.
• Strong experience with:
  Python; ML frameworks (PyTorch, Tensor Flow, scikit‑learn); LLMs/GenAI tooling; MLOps practices (pipelines/automation); cloud platforms (Azure/AWS/GCP).
• Familiarity with vector databases, embedding strategies, and RAG & graph architectures.
• Proven ability to design governed, observable, and secure AI systems.
• Experience in biotech/life sciences/healthcare or other GxP‑relevant domains.
• Extensive experience with enterprise SDLC and production IT processes; full SDLC delivery of AI systems.
• Experience implementing GenAI in enterprise or regulated environments.
• Exposure to AI governance, risk assessments, or validation frameworks.