Senior Python Engineer (Agentic AI)

We are seeking a Senior Python Engineer (Agentic AI) to build and maintain production-ready services that orchestrate LLM agents across distributed environments. This role focuses on multi-agent communication and delegation using the A2A protocol, asynchronous processing, and streaming APIs that deliver results incrementally. The ideal candidate combines hands-on experience with multi-agent architectures and Python's asynchronous capabilities with expertise in LLM integrations, observability, containerization, and secure service-to-service communication.

Key Responsibilities

• Build and maintain Python services that orchestrate LLM agents, including routing and delegation across multiple agents.
• Implement agent-to-agent communication using the A2A protocol and agent cards.
• Develop streaming APIs that deliver results to users incrementally.
• Integrate LLMs through platforms such as AWS Bedrock/Anthropic and Azure OpenAI, using tool calling and prompt templates.
• Implement observability and LLM tracing, troubleshooting issues across distributed environments.
• Package and deploy services in containers and Kubernetes clusters.
• Secure service-to-service communication using mTLS and manage certificate lifecycles.
• Contribute to CI/CD pipelines and version control best practices.

What we'd like to see in a candidate:

• Strong Python development skills.
• Solid experience with asyncio, including async generators, queues, concurrent producers, and task cancellation.
• Experience building streaming APIs that return results incrementally.
• Familiarity with agentic frameworks such as Pydantic AI, LangGraph, or comparable tools.
• Experience with tool calling, agent routing, and prompt templating using Jinja2.
• Experience integrating LLM APIs through AWS Bedrock/Anthropic, Azure OpenAI, or comparable platforms.
• Experience with observability using OpenTelemetry and LLM tracing tools such as Arize Phoenix or comparable solutions.
• Ability to debug logs across multiple pods using Splunk, Datadog, or similar tools.
• Experience with Docker, including multi-stage builds, private registries, and devcontainers.
• Hands-on experience with Kubernetes deployments, services, ingress, probes, kubectl, and k9s.
• Experience using mTLS as a client and managing certificate lifecycles.
• Familiarity with Git and GitHub Actions.
• Excellent communication skills and a high level of English proficiency.

Nice to Have

• Experience with Software Lifecycle (SLC) documentation, particularly in regulated environments.
• Background in pharmaceutical, healthcare, or life sciences projects.
• Proven hands-on experience with the A2A protocol and multi-agent delegation.