AI and Agentic Engineer II
Exelixis
August 29, 2026
On-site
Alameda, CA
IT
Summary/Job Purpose
The Engineer II - AI and Agentic designs, builds, and deploys production-grade agentic applications that automate multi-step IT and business workflows.
Essential Duties/Responsibilities
- Design and deliver end-to-end agentic applications and multi-agent workflows that automate IT/business processes.
- Convert prototypes into production-ready services with specifications, modular design, robust error handling, and test coverage.
- Integrate LLMs, prompts, tools, APIs, and enterprise data sources (e.g., AWS Bedrock, Databricks Mosaic AI, MCP servers).
- Implement CI/CD, automated evaluation harnesses, observability, and artifact versioning for AI/agent workloads.
- Author architecture notes, runbooks, SOPs, and technical documentation.
- Lead prompt/spec reviews, code reviews, threat modeling, and release-readiness gates.
- Apply secure-by-design and responsible AI principles (data governance, privacy, safe prompting).
- Contribute reusable components, patterns, and internal libraries.
- Partner with product, security, and platform teams to deliver scalable solutions.
Qualifications
- Bachelor’s in CS/AI/Data Science/Software Engineering (or related) + 5 years, OR Master’s + 3 years, OR equivalent combination.
- 2–4 years relevant experience building/contributing to LLM-enabled applications/agentic workflows and API integrations.
Required Skills
- Intermediate–advanced Python and/or JavaScript/TypeScript; modular design, testing, packaging.
- Agent frameworks/patterns (tool use, planning/execution loops, guardrails, evaluation, prompt/version management).
- REST APIs; OAuth/JWT, secrets management, secure data handling.
- CI/CD, unit/integration testing, linting, observability.
- Security best practices, architecture principles, responsible AI/data governance.
- Cloud AI platforms (prefer AWS Cloud/AWS Bedrock/Databricks Mosaic AI) and vector stores/RAG.
- AI-native engineering (Claude Code/Codex/GitHub Copilot or similar).
Preferred
- Evaluation frameworks, red-teaming, or LLM observability tooling.
The Engineer II - AI and Agentic designs, builds, and deploys production-grade agentic applications that automate multi-step IT and business workflows.
Essential Duties/Responsibilities
- Design and deliver end-to-end agentic applications and multi-agent workflows that automate IT/business processes.
- Convert prototypes into production-ready services with specifications, modular design, robust error handling, and test coverage.
- Integrate LLMs, prompts, tools, APIs, and enterprise data sources (e.g., AWS Bedrock, Databricks Mosaic AI, MCP servers).
- Implement CI/CD, automated evaluation harnesses, observability, and artifact versioning for AI/agent workloads.
- Author architecture notes, runbooks, SOPs, and technical documentation.
- Lead prompt/spec reviews, code reviews, threat modeling, and release-readiness gates.
- Apply secure-by-design and responsible AI principles (data governance, privacy, safe prompting).
- Contribute reusable components, patterns, and internal libraries.
- Partner with product, security, and platform teams to deliver scalable solutions.
Qualifications
- Bachelor’s in CS/AI/Data Science/Software Engineering (or related) + 5 years, OR Master’s + 3 years, OR equivalent combination.
- 2–4 years relevant experience building/contributing to LLM-enabled applications/agentic workflows and API integrations.
Required Skills
- Intermediate–advanced Python and/or JavaScript/TypeScript; modular design, testing, packaging.
- Agent frameworks/patterns (tool use, planning/execution loops, guardrails, evaluation, prompt/version management).
- REST APIs; OAuth/JWT, secrets management, secure data handling.
- CI/CD, unit/integration testing, linting, observability.
- Security best practices, architecture principles, responsible AI/data governance.
- Cloud AI platforms (prefer AWS Cloud/AWS Bedrock/Databricks Mosaic AI) and vector stores/RAG.
- AI-native engineering (Claude Code/Codex/GitHub Copilot or similar).
Preferred
- Evaluation frameworks, red-teaming, or LLM observability tooling.