Ambush is a people first company where talented, thoughtful individuals come together to build meaningful products and lasting partnerships. We believe the best work happens when people feel supported, trusted and empowered to bring their full abilities to the table.
Since 2015, because of our people first and long term mindset, we have grown into a partner relied on by some of the best companies in the world. We combine strong engineering, design and strategy with a growing strength in AI to help our clients see what is next and achieve bigger outcomes.
At the heart of everything we do is our team. We collaborate, take risks, lift each other up and take pride in doing work the right way, not settling for a quick makeshift solution. If you join Ambush, you join a group of people who want you to succeed and who show up for each other every day.
We believe in and expect real teamwork, a constant drive to be better, and delivering meaningful long term outcomes that we can be proud of together.

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
Instrument the application with observability and LLM tracing, and troubleshoot issues in distributed environments
Package and deploy services in containers and Kubernetes clusters
Secure service-to-service communication (mTLS) and manage the certificate lifecycle
Contribute to CI/CD pipelines and version control best practices

What we'd like to see in a candidate:

Strong Python skills
Proven hands-on experience with the A2A protocol and multi-agent delegation (essential for this role)
Solid experience with asyncio: async generators, queues, concurrent producers, and task cancellation
Experience building streaming APIs that return results incrementally
Agentic frameworks such as Pydantic AI, LangGraph, or comparable tools
Tool calling, agent routing, and prompt templating with Jinja2
LLM API integration (Bedrock/Anthropic, Azure OpenAI, or comparable platforms)
Observability with OpenTelemetry and LLM tracing tools (Arize Phoenix or comparable)
Log debugging across multiple pods (Splunk, Datadog, or similar)
Docker (multi-stage builds, private registries, devcontainers)
Kubernetes: deployments, services, ingress, probes, kubectl, and k9s
mTLS as a client and certificate lifecycle management
Git and GitHub Actions
Excellent communication skills, and a great level of English

Nice to haves:

Experience with Software Lifecycle (SLC) documentation, especially in regulated environments
Background in pharmaceutical, healthcare, or life sciences projects

Senior Python Engineer (Agentic AI)

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