As a
Senior AI Platform Engineer in the Developer Experience (DevEx) team, you will be a founding member of a specialized unit acting as the
“Engineering Intelligence” center for our R&D organization.
Your mission is to architect the foundational architecture for AI-native workflows. You won't just be using AI; you will be building the primitives and abstractions—agentic frameworks, commands, and skills—that redefine how hundreds of engineers interact with the codebase. This role balances deep technical systems building with a data-driven "influence over ownership" model, using engineering health metrics to pioneer new R&D standards.
Job Responsibilities And Expectations
Agentic Orchestration & Design
Architect and implement
complex AI-native workflows, including:
- Multi-agent systems and orchestration.
- Codebase-specific RAG (Retrieval-Augmented Generation).
- Custom evaluation loops to ensure high-quality AI-assisted outputs.
Workflow Abstractions
Define the standard "language of work" for R&D by designing core constructs for
Skills, Agents, and Commands that automate and reinvent the developer lifecycle (from specification and planning to implementation).
Ecosystem Integration
Leverage and integrate with the
Model Context Protocol (MCP) to connect the internal toolchain—including task management, version control, and documentation—into a unified, actionable context for AI agents.
Internal Marketplace & Tooling
Lead the evolution of an internal developer marketplace|framework and a multi-target CLI. You will build foundational development skills and commands while ensuring the platform remains secure, governed, and extensible for other teams.
Engineering Intelligence
Define and produce the telemetry and signals required to track engineering health (e.g., DORA and SPACE metrics). You will use data from engineering metrics platforms to
identify bottlenecks and perform "cost of inaction" analysis.
Technical Advocacy
Drive the adoption of AI-native workflows by:
- Leading Proof of Concepts (PoCs).
- Managing feedback loops with stakeholders.
- Acting as a consultant to other R&D teams to influence their performance and adoption.
AI-Native Inner Loop Optimization
Lead the evolution of the developer cycle by optimizing the agentic development flow. You will:
- Ensure high-fidelity context availability through seamless integrations.
- Refine the human-AI interaction model to minimize friction.
- Ensure the agentic workflow is significantly faster and more accurate than manual execution.
Requirements
- Professional Background: 5+ years of software engineering experience (Backend, DevOps, or Platform) with a track record of building maintainable, scalable tools for other developers.
- AI Specialization: 2+ years of hands-on experience in the AI/Agentic space. You must have deep context on LLM orchestration, agentic framework design, and the integration of AI into developer workflows.
- Polyglot Adaptability: High proficiency across a broad toolset. You are comfortable moving between Bash, PowerShell, Python, JavaScript/TypeScript, and Go (or similar) depending on the needs of the environment or integration.
- Technical Conceptual Depth: Deep understanding of MCP (Model Context Protocol), plugin architectures, and the design of developer-facing Skills, Agents, and Commands.
- Systems Thinking: A deep understanding of the developer lifecycle and the ability to translate complex manual processes into automated, agentic constructs.
- Strategic Communication: Proven ability to collect feedback, drive meetings, and influence other teams’ roadmaps through technical credibility and data-driven arguments.
Ways to Stand Out
- Experience building and maintaining internal-facing developer products or CLI tools used at scale.
- A background in Platform Engineering with a heavy focus on developer productivity and telemetry.
- Active participation or contributions to the open-source AI or Agentic ecosystem.