Senior AI/ML Engineer, Production AI (Contractor)
About the position Legend Biotech is a global biotechnology company dedicated to treating, and one day curing, life-threatening diseases. Headquartered in Somerset, New Jersey, we are developing advanced cell therapies across a diverse array of technology platforms, including autologous and allogenic chimeric antigen receptor T-cell, T-cell receptor (TCR-T), and natural killer (NK) cell-based immunotherapy. From our three R&D sites around the world, we apply these innovative technologies to pursue the discovery of safe, efficacious and cutting-edge therapeutics for patients worldwide. Legend Biotech entered into a global collaboration agreement with Janssen, one of the pharmaceutical companies of Johnson & Johnson, to jointly develop and commercialize ciltacabtagene autolecuel (cilta-cel). Our strategic partnership is designed to combine the strengths and expertise of both companies to advance the promise of an immunotherapy in the treatment of multiple myeloma. Legend Biotech is seeking a Senior AI/ML Engineer, Production AI (Contractor) as part of the IT team based in Somerset, NJ. Role Overview We are seeking a Senior AI/ML Engineer with strong experience delivering production-grade ML and Generative AI solutions. In this role you will do model development, design, deploy, monitor, and govern enterprise-ready ML and GenAI systems that are scalable, auditable, and compliant with internal AI policies and regulatory expectations. You will help establish MLOps and GenAI Ops foundations, including evaluation, observability, and Responsible AI controls, enabling safe adoption of both predictive ML and GenAI use cases across the organization. Responsibilities • Design, build, and deploy production-grade ML and Generative AI solutions, moving from prototypes to hardened services. • Implement GenAI patterns such as: Retrieval-augmented generation (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. • Build or configure end-to-end MLOps and GenAI Ops frameworks covering: Model and prompt versioning Reproducible pipelines and CI/CD for ML and GenAI workloads Controlled deployment and rollback strategies • Integrate AI workflows with enterprise data platforms, orchestration tools, and cloud infrastructure • Define evaluation frameworks for both ML and GenAI, including: Model accuracy, robustness, and drift LLM response quality, grounding, hallucination risk, and safety checks Bias, fairness, and explainability assessments • Establish acceptance criteria and validation artifacts suitable for regulated and audit-ready environments • Implement observability frameworks for ML and GenAI systems to monitor: Model and LLM performance degradation Data and embedding drift Prompt and response behavior over time Latency, failure modes, and usage patterns • Enable full logging and traceability to support investigations, audits, and continuous improvement • Embed Responsible AI principles across the AI lifecycle, including: Human-in-the-loop controls for GenAI-assisted workflows Transparency, explainability, and proper-use disclosures Strong data privacy, access control, and lineage discipline • Ensure GenAI features are opt-in, governed, and aligned with Legend’s AI policies and regulatory expectations • Partner with Data Engineering, Architecture, Security, QA, and Business teams • Translate business problems into well-scoped, governed AI and GenAI solutions • Contribute to enterprise AI standards, reference architectures, and platform roadmaps Requirements • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field • 5+ years of hands-on experience deploying ML systems in production • Strong experience with: Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn) LLMs and GenAI tooling (commercial or open-source) MLOps practices, pipelines, and automation Cloud platforms (Azure, AWS, or GCP) • Familiarity with vector databases, embedding strategies, and RAG & graph architectures • Proven ability to design governed, observable, and secure AI systems • Extensive experience operating within enterprise SDLC and production IT processes. • Demonstrated experience delivering AI systems through full system development lifecycle (SDLC). • Experience implementing GenAI in enterprise or regulated environments. • Exposure to AI governance, risk assessments, or validation frameworks. Nice-to-haves • Experience in biotech, life sciences, healthcare, or other GxP-relevant domains