Director, AI and Agentic Engineering

Exelixis•Alameda, CA

1d•$215,000 - $306,000•Onsite

About The Position

The Director, Engineering AI and Agentic leads the strategy, architecture, delivery, and operations of enterprise AI platforms and AI-enabled business solutions. This role combines deep AI engineering leadership with strong product management capabilities to translate business needs into scalable, secure, measurable, and user-centered AI products. The Director, Engineering AI And Agentic is accountable for building reusable AI platform capabilities, delivering production-grade agentic and generative AI solutions, advancing AI-native engineering practices, and partnering across IT, security, data, legal, compliance, and business stakeholders to responsibly scale AI adoption across Exelixis.

Requirements

Bachelor’s degree in a related discipline and 13 years of related experience; or Master’s degree in a related discipline and 11 years of related experience; or Equivalent combination of education and experience.
5+ years in AI Engineering or Cloud platform Engineering
Leadership experience, overseeing cross-functional teams
Experience in full lifecycle system development (design, implementation, validation and support) of cloud based solutions and platforms
Advanced understanding of AI engineering, generative AI, agentic AI patterns, AI platform architecture, software engineering, cloud-native design, enterprise integration, and production operations.
Strong ability to define platform strategy, product roadmaps, business cases, success metrics, delivery plans, and adoption approaches for enterprise AI capabilities.
Hands-on technical credibility with modern AI solution patterns, including LLMs, prompt/context engineering, tool use, multi-step workflows, agent orchestration, retrieval-augmented generation, vector search, evaluation, monitoring, and guardrails.
Experience with cloud AI and data platforms such as AWS, Amazon Bedrock, Databricks Mosaic AI, or equivalent technologies; familiarity with APIs, event-driven architectures, data pipelines, identity, secrets management, and enterprise security patterns.
Demonstrated ability to lead AI-native engineering practices using coding agents, AI-assisted development tools, specification-driven development, automated testing, CI/CD, DevSecOps, observability, and continuous improvement.
Strong understanding of responsible AI, data governance, privacy, cybersecurity, model risk, auditability, human-in-the-loop controls, regulatory expectations, and enterprise policy compliance.
Excellent leadership skills with the ability to recruit, mentor, coach, and develop diverse technical teams while setting clear standards for quality, accountability, innovation, and delivery excellence.
Strong communication, executive presence, facilitation, stakeholder management, and influencing skills; able to translate complex AI concepts into business implications, risks, tradeoffs, and decisions.
Ability to work effectively in ambiguous and fast-changing environments, make informed tradeoffs, resolve conflicts, and align cross-functional teams around outcomes.
Advanced planning, portfolio management, vendor management, financial management, and execution skills with the ability to manage multiple initiatives across a matrixed organization.

Nice To Haves

Knowledge of life sciences, biotechnology, clinical, regulatory, GxP, SOX, 21 CFR Part 11, or validated system environments is preferred.

Responsibilities

Define and execute the AI engineering strategy, roadmap, and operating model for enterprise AI platforms, reusable services, and AI-enabled business solutions.
Lead the design and delivery of scalable AI platform capabilities, including model access patterns, agent orchestration, retrieval-augmented generation, evaluation frameworks, prompt and artifact management, observability, governance controls, and reusable integration patterns.
Oversee end-to-end development of production-grade generative AI, agentic AI, and automation solutions that improve productivity, decision support, operational efficiency, and business outcomes across functions.
Apply strong product management practices, including opportunity assessment, stakeholder discovery, prioritization, roadmap planning, user experience definition, value measurement, adoption planning, and lifecycle management.
Partner with business leaders, product managers, architects, data teams, cybersecurity, privacy, legal, compliance, and enterprise application teams to translate strategic business needs into secure, governed, and scalable AI solutions.
Establish AI-native engineering standards and practices, including specification-driven development, agent-assisted software delivery, automated testing, code quality, reusable patterns, DevSecOps, CI/CD, release management, and operational support models.
Ensure AI solutions are designed for reliability, security, scalability, auditability, explainability, human oversight, measurable autonomy, and responsible AI use consistent with company policies and regulatory expectations.
Manage platform, and technology decisions across AI providers, cloud services, data platforms, development tools, and open-source components; assess build-versus-buy options and total cost of ownership.
Create and monitor success metrics for AI platforms and solutions, including adoption, productivity impact, quality, latency, cost, risk reduction, reuse, user satisfaction, and business value realization.
Lead architecture reviews, prompt and solution reviews, security reviews, model and agent evaluations, release-readiness assessments, and post-production performance monitoring.
Develop talent, delivery practices, technical documentation, playbooks, reference architectures, and enablement materials that help teams adopt AI-native engineering safely and effectively.
Performs other duties as assigned
Complies with all policies and standards