Senior Software Engineer, AI Gateway

About Docker
Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default.
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Docker AI Gateway is our answer to the complexity of taking AI agents from prototype to production. It’s a powerful, intelligent, and secure control point that eliminates the toil of model orchestration, tool management, observability, and governance—so developers can focus on building incredible AI agents, not gluing together infrastructure.
The Gateway sits at the center of modern AI applications, offering:
A model and tool routing layer with built-in security and cost optimization

A familiar OpenAI-compatible interface and MCP server

Unified observability and policy enforcement

Auto-RAG, tool injection, session summarization, and more

We’re just getting started—and we need exceptional engineers to help us build the backbone of the future of agent-based development.

What You’ll Do
Design and implement core systems powering the AI Gateway, including the model router, MCP gateway, and control plane

Build infrastructure that supports dynamic model selection/routing, auto-failover, cost-based routing, and policy enforcement

Own critical capabilities such as secure credential storage, session summarization, caching, and rate limiting

Develop APIs for developers building with OpenAI-compatible interfaces and the Model Context Protocol

Build the underlying infrastructure to support evaluation, telemetry, replay, and backtesting for agents and LLM workflows

Lead architectural decisions and mentor engineers as the team scales

Performance tuning - caching, session summarization, and rate limiting that keep the gateway fast under real production load

Contribute to roadmap planning, technical strategy, and cross-functional alignment

Key Problems You’ll Help Solve
Building a unified abstraction over fundamentally different model and tool providers without leaking their differences into the developer experience

Making credential and identity handling for tool access both airtight and fast

Giving developers real-time and historical visibility into what their agents are doing, with policy enforcement that can't be bypassed

Keeping latency low and reliability high as traffic scales from early adopters to production fleets

Who You Are
Required Qualifications:
6+ years of backend engineering experience with production-grade systems

Deep knowledge of distributed and highly scalable systems, cloud-native infrastructure, and API design

Experience building secure, high-throughput services (e.g., gateways, proxies, load balancers, policy engines)

Fluency in Go, and/or Rust (both preferred)

Familiarity with AI/ML platforms or model serving infrastructure

A strong product mindset—you're excited about building developer-facing tools

Ownership mentality with a bias for shipping, learning, and iterating

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience

Bonus Qualifications:
Prior experience with OpenAI, Anthropic, or similar LLM APIs

Familiarity with RAG architectures, vector databases, or agent frameworks (e.g., LangChain, AutoGPT, CrewAI)

Experience with policy engines (e.g., OPA), observability frameworks (e.g., OpenTelemetry), or API gateways

Understanding