Lead AI Engineer (Agentic AI)

You bring technical strength, curiosity, and a drive to build meaningful AI systems. Here is what helps you succeed in this role:

• A strong Python engineer who has shipped production systems, not just prototypes
• Hands-on with agentic frameworks (LangChain, LangGraph, Agno, or similar)
• Fluent in LLMs in production: prompting, tool use, orchestration, failure modes
• Experience building production RAG systems: retrieval architectures, vector databases, embedding strategies, and evaluation frameworks
• Experience with agent evaluation frameworks and observability tooling (e.g. LangSmith or similar): tracing agent behavior, debugging multi-agent interactions, and ensuring reliability in production
• Comfortable with cloud environments (AWS, GCP, or Azure) and distributed system design
• Experienced with MLOps practices: CI/CD, monitoring, deployment pipelines
• Autonomous, low ego, and serious about what you build
• Excited about working in a domain where the quality of your work has real-world consequences

• Experience in life sciences, regulated industries, or GxP-compliant environments
• Experience with multi-agent systems and workflow orchestration

You will own and evolve the agentic layer that powers our products. That means designing and building the multi-agent orchestration systems, RAG pipelines, tool-use architectures, and workflow engines that sit at the core of what we build.

This is a senior engineering role with real production stakes. You will make foundational architecture decisions, shape engineering standards, and work directly with product and leadership from day one. It is not a research role. It is a building role.

Key Responsibilities:

• Design and implement multi-agent architectures using Agno, LangChain, and LangGraph
• Build and maintain scalable backend services and APIs in Python and FastAPI
• Own agent orchestration logic: task routing, tool use, state management, and human-in-the-loop integration
• Design and implement RAG pipelines over domain-specific data, with strict requirements for accuracy, traceability, and compliance
• Make production-grade decisions on LLM integration, prompting strategies, and reliability patterns
• Set engineering standards for code quality, testing, and deployment of agentic systems
• Drive end-to-end feature ownership from design through to production
• Mentor engineers and actively shape the engineering culture as the team grows
• Collaborate closely with product and leadership to influence the technical roadmap

We know that great candidates might not check every box. If you're excited about this role and believe you could contribute, we'd love to hear from you.