Responsibilities
- Design, build, and deploy agentic AI workflows that automate and transform complex business processes, leveraging multi-agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, or equivalent)
- Architect and implement MCP servers to expose enterprise tools, APIs, and data sources as standardized capabilities consumable by AI agents
- Connect multi-agent systems to enterprise databases, internal APIs, and MCP servers to enable grounded, context-aware, and action-oriented AI solutions
- Partner cross-functionally with internal teams to define data contracts, lineage standards, and quality thresholds required for AI/ML use cases
- Design and implement agentic memory systems (short-term, long-term, episodic) and planning/reasoning loops to support reliable autonomous task execution
- Evaluate agentic system performance across accuracy, reliability, latency, cost, and safety dimensions using structured benchmarks and red-teaming methodologies
- Build and maintain guardrail frameworks (input/output filtering, content moderation, policy enforcement, hallucination detection) to ensure the safety, compliance, and trustworthiness of GenAI and agentic solutions
- Develop retrieval-augmented generation (RAG) pipelines, including chunking strategies, embedding models, vector store selection, and retrieval optimization for enterprise knowledge bases
- Apply prompt engineering, few-shot learning, and fine-tuning techniques to adapt foundation models for domain-specific pharma use cases
- Design, develop, validate, and deploy traditional machine learning models (classification, regression, clustering, time-series, survival analysis) to address structured business problems
- Build and maintain end-to-end ML pipelines adhering to LLM Ops / ML Ops standards including model registry, evaluation benchmarks, prompt/version control, observability, and rollback procedures
- Other responsibilities as assigned
Requirements
- Master’s or PhD in Machine Learning, Computer Science, Data Science, Information Systems, or a related quantitative discipline
- Minimum of 7 years of experience in AI/ML engineering
- At least 3 years of hands-on experience with Generative AI and agentic AI systems
- Expertise in multi-agent frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar technologies
- Experience building MCP servers and integrating AI systems with enterprise data sources, APIs, and tools
- Strong experience in RAG pipeline development, embedding models, and vector database technologies
- Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face
- Experience implementing ML Ops or LLM Ops practices, including model lifecycle management, evaluation, and deployment
- Ability to travel domestically and internationally as required
Nice to Have
- Experience in working with real-world data (RWD), claims data, EHR data, Clinical Study data, translational and biological data and the corresponding databases
Work Arrangement
Hybrid — San Diego, CA, South San Francisco, CA, Princeton, NJ
Additional Information
- Ability to travel domestically and internationally as required
- Regular standing, walking, sitting, and use of hands for handling or operating equipment
- May need to reach, climb, balance, stoop, kneel, crouch
- Maintain visual, verbal, and auditory communication in standard office environment and while working independently from remote locations
- Occasionally lift and/or move up to 20 pounds
- Ability to travel independently overnight and/or work after hours as required by travel schedules or business needs
