Agentic AI Systems Engineer

As an Agentic AI Systems Engineer, you will design and build the platform foundations that enable autonomous, always-on AI agents to collaborate with engineers at scale. This role is for engineers who think in systems, abstractions, and long-term leverage—not just features.

You will work at the intersection of developer platforms, distributed systems, and applied AI, shaping how agentic engineering operates across the organization.

Key Platform Areas

MCP Platform Layer
Build and evolve the Model Context Protocol (MCP) layer that provides AI agents with structured, secure, and low-latency access to internal tools, data, documentation, and the developer's lifecycle.

Skill Marketplace
Design systems that convert tribal knowledge into composable, portable “skills.” These skills can be loaded by agents on demand, making any engineer or agent productive in any repository instantly.

What You Will Build

• Skill Pack Framework
  Architect and ship the end-to-end skill framework, including authoring, versioning, distribution, and runtime loading across Java, Python, C++, and web-based codebases. Skills are the core differentiator—encapsulated expertise that travels with the agent.

• Multi-Agent Runtime
  Design and implement a multi-agent execution environment with persistent background agents, swarm coordination, and cross-agent context sharing. Enable agents to operate continuously and in parallel, not as one-shot invocations.

• Agent-Aware Code Intelligence
  Develop deep code understanding systems such as semantic code search, dependency-aware context modeling, and structural codebase analysis. Move beyond text-based retrieval toward true program-level reasoning.

Responsibilities

• Own and evolve the MCP platform, unifying code, documentation, and developer lifecycle MCPs into a single, dependable context infrastructure.
• Partner with product, domain, service, and backend platform teams to deliver foundational agentic systems.
• Define technical direction for agentic infrastructure across domain and shared platforms.
• Establish architecture standards, verification methods, and trust models for agent-generated code at organizational scale.
• Mentor engineers in agentic development practices and raise the bar for how teams build and ship with AI agents.

Must-Have Qualifications

• Proven experience building and operating developer tooling or platform infrastructure (SDKs, CLIs, APIs, runtimes) in large-scale engineering environments.
• Strong proficiency in at least one major programming language (e.g., Java, Python, C++) with hands-on experience integrating AI/ML models into developer workflows.
  • Solid system design expertise for highly reliable, observable, developer-facing services and agentic systems.