Director, AI Engineering

Acadia Pharmaceuticals•San Diego, NJ

5d•Hybrid

About The Position

Acadia Pharmaceuticals is committed to turning scientific promise into meaningful innovation that makes the difference for underserved neurological and rare disease communities around the world. Our commercial portfolio includes the first and only FDA-approved treatments for Parkinson’s disease psychosis and Rett syndrome. We are developing the next wave of therapeutic advancements with a robust and diverse pipeline that includes mid- to late-stage programs in Alzheimer’s disease psychosis and Lewy body dementia psychosis, along with earlier-stage programs that address other underserved patient needs. At Acadia, we’re here to be their difference. This hybrid position requires onsite attendance three days per week at one of our office locations in San Diego, CA, South San Francisco, CA, or Princeton, N J Position Summary The Director, AI Engineering is a strategic technology leader responsible for shaping and advancing the enterprise AI application and agent ecosystem. This role owns the AI platform and agent framework layer that enables scalable, secure, and impactful AI-powered solutions across the organization. The successful candidate will drive the development of enterprise-grade capabilities including reusable agent templates, multi-agent orchestration, an internal agent publishing platform, self-service RAG solutions, tool and skill registries, prompt management, and evaluation and output validation frameworks. This leader will balance deep technical expertise with a passion for creating intuitive, low-code experiences that empower business users to independently build and deploy AI solutions. Partnering closely with IT, Cybersecurity, Enterprise Architecture, and business stakeholders, the Director will ensure the AI platform remains secure, governed, scalable, and aligned with organizational objectives while delivering measurable business value and accelerating AI adoption across the enterprise.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Machine Learning, Artificial Intelligence, or a related technical discipline required; advanced degree preferred
8+ years of experience in AI/ML, software engineering, or a related field, including 4+ years of current, hands-on experience building production-grade agentic AI systems and LLM-powered applications
Experience delivering self-service or platform tooling adopted by non-technical users
Expert-level proficiency with at least two major agent frameworks such as LangGraph, CrewAI, Anthropic Agents SDK, or OpenAI Agents SDK, including framework customization and development
Expert-level experience designing and implementing RAG solutions, including chunking strategies, embedding selection, vector databases, hybrid retrieval, and reranking methodologies
Hands-on experience developing enterprise AI capabilities including evaluation frameworks, guardrails, agent memory systems, human-in-the-loop workflows, tool registries, skill registries, and prompt management platforms
Experience building or integrating Model Context Protocol (MCP) servers and exposing enterprise capabilities to AI agents
Proficiency in Python and machine learning frameworks such as PyTorch, scikit-learn, and Hugging Face Transformers, with a strong understanding of LLM architectures and traditional machine learning techniques
Experience leveraging AI-assisted development tools to accelerate software delivery, automate code generation, and improve engineering productivity
Experience working within regulated environments and AI governance frameworks, including GxP, NIST AI RMF, EU AI Act, or similar compliance standards
Biopharmaceutical industry experience, including familiarity with commercial, medical, research, clinical, or market access functions and relevant healthcare data sources
Willingness and ability to travel domestically and internationally as needed
An equivalent combination of education and relevant experience may be considered
Other duties as assigned

Responsibilities

Architect and maintain the enterprise agent platform - the system through which agents are developed, published, versioned, and coordinated, using frameworks such as LangGraph, CrewAI, Anthropic Agents SDK, and OpenAI Agents SDK.
Engineer multi-agent coordination patterns enabling agents with distinct roles, data access, and functional scope to collaborate reliably on complex, multi-step business workflows.
Design and implement reusable agent templates, composable orchestration primitives, and agentic memory systems (short-term, long-term, episodic) that development teams and non-engineers can configure and deploy.
Build human-in-the-loop (HITL) escalation workflows and clearly defined agent action boundaries to ensure appropriate human oversight at high-risk decision points.
Lead the design and delivery of a self-service RAG builder, including ingestion pipelines, chunking, embedding, vector store integration, hybrid retrieval, and reranking surfaced through a low-code interface accessible to non-technical users.
Build and operate an enterprise tool and skill registry: a versioned catalog of APIs, functions, MCP servers, and data connectors that agents can securely discover and invoke, with schema contracts, authentication, and access controls.
Establish and manage the enterprise prompt management system with versioning, governance workflows, A/B testing, rollback capabilities, and domain-specific prompt libraries for commercial, medical, and R&D functions.
Own the evaluation and benchmarking methodology for agentic systems. Design rigorous, quantitative eval suites covering accuracy, faithfulness, groundedness, task completion, latency, cost, and safety. Implement automated regression detection in production.
Design and implement multi-layer guardrail frameworks including input/output validation, content moderation, hallucination detection, policy enforcement, and agent permission scoping aligned with AI governance policy and regulatory requirements.
Apply product thinking and user-centered design to platform tooling; track adoption metrics, gather user feedback, and iterate to reduce friction for non-engineer stakeholders across Commercial, Medical Affairs, R&D, and Corporate Functions.
Contribute to the enterprise AI strategy and portfolio roadmap; participate actively in the AI Governance Council providing expertise on agentic risk, safety, and platform governance.
Ensure all engineered solutions comply with global AI regulations, ethical AI standards, data privacy requirements (HIPAA, GDPR), applicable GxP processes, and enterprise security standards.
Mentor engineers and foster a culture of rigorous evaluation, responsible experimentation, and continuous improvement across the AI team.
Other responsibilities as assigned.