Associate Director, AI/ML Engineering
Acadia Pharmaceuticals
3 days ago
Remote friendly (Princeton, NJ)
United States
IT
Position Summary
- Hands-on technical leader driving the design, architecture, and delivery of Generative AI and agentic AI solutions across the enterprise, building scalable multi-agent systems, connecting AI solutions to enterprise data/tools, and ensuring safe, reliable deployment via evaluation and guardrails in a regulated biopharmaceutical environment.
Primary Responsibilities
- Design/build/deploy agentic AI workflows (e.g., LangGraph, AutoGen, CrewAI).
- Architect/implement MCP servers to expose enterprise tools, APIs, and data sources as AI-agent capabilities.
- Connect multi-agent systems to enterprise databases, internal APIs, and MCP servers for grounded, context-aware, action-oriented solutions.
- Define data contracts, lineage standards, and quality thresholds with cross-functional teams.
- Implement agentic memory systems (short/long-term, episodic) and planning/reasoning loops.
- Evaluate performance (accuracy, reliability, latency, cost, safety) using benchmarks and red-teaming.
- Build/maintain guardrail frameworks (filtering, moderation, policy enforcement, hallucination detection).
- Develop RAG pipelines (chunking, embeddings, vector stores, retrieval optimization).
- Use prompt engineering, few-shot learning, and fine-tuning for pharma use cases.
- Develop/validate/deploy ML models (classification, regression, clustering, time-series, survival analysis).
- Maintain end-to-end ML pipelines with LLM Ops/ML Ops (model registry, evals, prompt/version control, observability, rollback).
Education/Experience/Skills
- Master’s or PhD in ML/CS/Data Science/IS or related quantitative field.
- 7+ years AI/ML engineering; 3+ years hands-on Generative/agentic AI.
- Multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, etc.).
- MCP server building; enterprise data/API/tool integration.
- RAG, embeddings, vector databases.
- Python; PyTorch/TensorFlow/scikit-learn/Hugging Face.
- LLM Ops/ML Ops; model lifecycle, evaluation, deployment.
- Ability to travel domestically/internationally.
Benefits (explicitly listed)
- Competitive base plus discretionary bonus and equity; medical/dental/vision; 401(k) 1:1 up to 5%; ESPP; 15+ vacation days; paid holidays; sick time; paid parental leave; tuition assistance.
- Hands-on technical leader driving the design, architecture, and delivery of Generative AI and agentic AI solutions across the enterprise, building scalable multi-agent systems, connecting AI solutions to enterprise data/tools, and ensuring safe, reliable deployment via evaluation and guardrails in a regulated biopharmaceutical environment.
Primary Responsibilities
- Design/build/deploy agentic AI workflows (e.g., LangGraph, AutoGen, CrewAI).
- Architect/implement MCP servers to expose enterprise tools, APIs, and data sources as AI-agent capabilities.
- Connect multi-agent systems to enterprise databases, internal APIs, and MCP servers for grounded, context-aware, action-oriented solutions.
- Define data contracts, lineage standards, and quality thresholds with cross-functional teams.
- Implement agentic memory systems (short/long-term, episodic) and planning/reasoning loops.
- Evaluate performance (accuracy, reliability, latency, cost, safety) using benchmarks and red-teaming.
- Build/maintain guardrail frameworks (filtering, moderation, policy enforcement, hallucination detection).
- Develop RAG pipelines (chunking, embeddings, vector stores, retrieval optimization).
- Use prompt engineering, few-shot learning, and fine-tuning for pharma use cases.
- Develop/validate/deploy ML models (classification, regression, clustering, time-series, survival analysis).
- Maintain end-to-end ML pipelines with LLM Ops/ML Ops (model registry, evals, prompt/version control, observability, rollback).
Education/Experience/Skills
- Master’s or PhD in ML/CS/Data Science/IS or related quantitative field.
- 7+ years AI/ML engineering; 3+ years hands-on Generative/agentic AI.
- Multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, etc.).
- MCP server building; enterprise data/API/tool integration.
- RAG, embeddings, vector databases.
- Python; PyTorch/TensorFlow/scikit-learn/Hugging Face.
- LLM Ops/ML Ops; model lifecycle, evaluation, deployment.
- Ability to travel domestically/internationally.
Benefits (explicitly listed)
- Competitive base plus discretionary bonus and equity; medical/dental/vision; 401(k) 1:1 up to 5%; ESPP; 15+ vacation days; paid holidays; sick time; paid parental leave; tuition assistance.