Associate Director, AI/ML Engineering
Acadia Pharmaceuticals
August 29, 2026
Remote friendly (San Diego, CA)
United States
IT
Position Summary:
The Associate Director, AI/ML Engineering serves as a hands-on technical leader driving the design, architecture, and delivery of Generative AI and agentic AI solutions across the enterprise. Builds scalable multi-agent systems, connects AI solutions to enterprise data and tools, and ensures safe, reliable deployment through robust evaluation and guardrail frameworks.
Primary Responsibilities:
- Design, build, and deploy agentic AI workflows using multi-agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI).
- Architect and implement MCP servers to expose enterprise tools, APIs, and data sources as standardized capabilities for AI agents.
- 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 for AI/ML use cases.
- Design agentic memory systems (short-term/long-term/episodic) and planning/reasoning loops for reliable autonomous execution.
- Evaluate performance (accuracy, reliability, latency, cost, safety) via structured benchmarks and red-teaming.
- Build guardrail frameworks (input/output filtering, moderation, policy enforcement, hallucination detection) for safety/compliance.
- Develop RAG pipelines (chunking, embeddings, vector stores, retrieval optimization).
- Apply prompt engineering, few-shot learning, and fine-tuning for domain-specific pharma use cases.
- Develop traditional ML models (classification, regression, clustering, time-series, survival analysis).
- Implement end-to-end ML pipelines per LLM Ops/ML Ops standards (model registry, evaluation, prompt/version control, observability, rollback).
- Plus: experience with RWD, claims, EHR, clinical study, translational/biological data.
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 AI/agentic AI.
- Expertise in multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar).
- Experience building MCP servers; integrating AI with enterprise data sources/APIs/tools.
- Strong RAG/embeddings/vector database experience.
- Python; PyTorch, TensorFlow, scikit-learn, Hugging Face.
- Experience with ML Ops/LLM Ops.
- Travel domestically/internationally as required.
The Associate Director, AI/ML Engineering serves as a hands-on technical leader driving the design, architecture, and delivery of Generative AI and agentic AI solutions across the enterprise. Builds scalable multi-agent systems, connects AI solutions to enterprise data and tools, and ensures safe, reliable deployment through robust evaluation and guardrail frameworks.
Primary Responsibilities:
- Design, build, and deploy agentic AI workflows using multi-agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI).
- Architect and implement MCP servers to expose enterprise tools, APIs, and data sources as standardized capabilities for AI agents.
- 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 for AI/ML use cases.
- Design agentic memory systems (short-term/long-term/episodic) and planning/reasoning loops for reliable autonomous execution.
- Evaluate performance (accuracy, reliability, latency, cost, safety) via structured benchmarks and red-teaming.
- Build guardrail frameworks (input/output filtering, moderation, policy enforcement, hallucination detection) for safety/compliance.
- Develop RAG pipelines (chunking, embeddings, vector stores, retrieval optimization).
- Apply prompt engineering, few-shot learning, and fine-tuning for domain-specific pharma use cases.
- Develop traditional ML models (classification, regression, clustering, time-series, survival analysis).
- Implement end-to-end ML pipelines per LLM Ops/ML Ops standards (model registry, evaluation, prompt/version control, observability, rollback).
- Plus: experience with RWD, claims, EHR, clinical study, translational/biological data.
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 AI/agentic AI.
- Expertise in multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar).
- Experience building MCP servers; integrating AI with enterprise data sources/APIs/tools.
- Strong RAG/embeddings/vector database experience.
- Python; PyTorch, TensorFlow, scikit-learn, Hugging Face.
- Experience with ML Ops/LLM Ops.
- Travel domestically/internationally as required.