Agentic AI Sr. Engineer

About the Company

Responsibilities

• ● 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 GitHub 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.
• ● 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.
• ● Solid Python and/or TypeScript 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).
• ● Experience with tool-integration layers that connect agents to enterprise data, models, and services.
• ● Experience with AI frameworks such as Pydantic-AI, LangChain, 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 considerations in a regulated environment. A life-sciences background is not required, but curiosity about biology and drug discovery is.