Senior AI/ML Engineer, Production AI (Contractor)

About the position reputed company 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 reputed company 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. reputed company entered into a global collaboration agreement with Janssen, one of the pharmaceutical companies of reputed company, to jointly reputed company 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. reputed company 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 reputed company (RAG). reputed company engineering and reputed company versioning. Embedding pipelines and vector search. Secure API-based model access.

• Ensure AI systems meet enterprise standards for scalability, performance, reliability, and reputed company.
• Build or configure end-to-end MLOps and GenAI Ops frameworks covering:

Model and reputed company 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 reputed company 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 reputed company reputed company and response behavior over time Latency, failure modes, and usage patterns

• reputed company full logging and traceability to support investigations, audits, and reputed company improvement
• Embed Responsible AI principles across the AI lifecycle, including:

Human-in-the-reputed company controls for GenAI-assisted workflows Transparency, explainability, and proper-use disclosures Strong data privacy, access control, and reputed company discipline

• Ensure GenAI features are opt-in, governed, and reputed company with Legend’s AI policies and regulatory expectations
• Partner with Data Engineering, Architecture, reputed company, 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 reputed company 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-reputed company) 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 reputed company 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.

reputed company-to-haves

• Experience in biotech, life sciences, healthcare, or other GxP-relevant domains

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