Agentic AI Sr. Engineer

• Design, build, and operate the agentic and LLM-powered systems that biologics scientists rely on, owning them from concept through reliable, monitored production use.
• Build custom agentic skills and tools that encode our scientists’ expertise, so an agent can do real domain work rather than generic chat.
• Connect agents to internal data, models, and services through AZ’s approved integration platforms, in line with AZ data and security standards.
• Build and deploy LLM and agentic applications end to end, including the interfaces and the supporting engineering (authentication, logging, evaluation, error handling) that makes a tool dependable.
• Build the digital pipelines that move data between our models, our high-throughput and automated lab platforms, and the scientists who use the results.
• Partner day to day with protein scientists, computational biologists, and platform engineers, and work in close partnership with BIX, EAI, and R&D IT, reusing shared platforms and data products rather than building in isolation.
• Document your work in Git Hub and Confluence so others can maintain and extend it, and meet relevant safety, quality, and compliance standards, including FAIR (Findable, Accessible, Interoperable, Reusable) data practices.

You will work in the CLI or the coding environment you prefer, such as VS Code, using code assistants and agents day to day to write and ship code more efficiently. Our stack includes Claude Code, Claude Cowork, Git Hub Copilot, M365 Copilot, and Hugging Face, with Git Hub and Confluence for code and documentation. You will deploy on AZ infrastructure including scientific computing platforms, AWS, Kubernetes, and Domino, with access to high-end GPU compute (including AZ’s sovereign AI compute platform built on NVIDIA DGX SuperPOD).

Essential Education & Experience

• MS degree in Computer Science, Software Engineering, Computational Biology, Data Science, or a related quantitative field, or equivalent demonstrated ability.
• 3–10 years of relevant software engineering experience.
• Proven, hands‐on experience building agentic AI systems and deploying them to production. We look for something you personally built and shipped that people used, whether at a company, a startup, a lab, or a substantial open‐source or personal project.

Essential Skills

• Solid Python and/or Type Script programming skills, and hands‐on experience in software development best practices: clean code, version control, testing, and the judgment to build something that keeps working after you have moved on.
• Practical experience building with LLMs and agent frameworks, including tool and function calling, retrieval, and orchestration of multi‐step workflows.
• Fluency with modern agentic developer tools such as Claude Code, Copilot, or similar, used daily as part of how you build.
• Clear written and verbal communication, with the ability to explain technical choices to scientists who are not engineers.
• Working knowledge of Unix, SQL databases, REST APIs, and cloud computing (AWS).

Desired Skills

• Experience with tool‐integration layers that connect agents to enterprise data, models, and services.
• Experience with AI frameworks such as Pydantic‐AI, Lang Chain, or similar.
• Experience building production retrieval‐augmented generation (RAG) pipelines and working with vector databases.
• Experience in at least one compiled programming language (e.g., C, Java, Go, or Rust).
• Experience with the evaluation and guardrails that make LLM applications trustworthy, and cloud deployment practice: containers, Kubernetes, CI/CD, and platforms such as Domino.
• Familiarity with laboratory automation, high‐throughput platforms, or scientific data, and awareness of responsible‐AI and compliance…