Agentic AI Engineer

Senior Agentic AI Engineer Embedded Engineering Engagement We are hiring Senior Agentic AI Engineers to join a high-impact engineering team building the next generation of AI-driven software testing and developer workflows. This is not a traditional ML research or model-training role . We are looking for strong software and systems engineers who know how to build with AI agents: designing agentic workflows, integrating agents into existing engineering systems, managing context effectively, and making AI-driven automation reliable enough for real-world use. The ideal engineer is hands-on, highly autonomous, comfortable navigating a large codebase, and able to take an ambiguous problem from concept through implementation. What You'll Work On You may work across several closely related areas, including: Agentic workflow integration — Integrating autonomous AI workflows into existing engineering and test infrastructure. AI-driven test automation — Improving systems that generate, execute, analyze, and maintain automated tests. Context management — Building systems that provide agents with the right code, history, tools, state, and information at the right time. Developer-agent tooling — Improving how AI agents interact with repositories, build systems, test infrastructure, and engineering workflows. Reliability and evaluation — Debugging agent behavior, identifying failure modes, and improving the consistency and usefulness of agent-generated results. Responsibilities Design, build, and integrate production-oriented agentic AI workflows. Develop tooling that enables AI agents to interact effectively with large codebases and engineering systems. Improve automated testing and test-generation workflows using LLMs and coding agents. Design context-management and retrieval strategies for long-running or complex agent workflows. Debug failures across agents, tools, builds, tests, and surrounding infrastructure. Build integrations between agentic systems and existing developer/test platforms. Evaluate agent output and develop mechanisms to improve reliability and reduce failures or unnecessary human intervention. Work directly with senior technical stakeholders to turn loosely defined problems into working systems. Own projects end-to-end and communicate progress, risks, and blockers clearly. What We're Looking For Required 5+ years of professional software engineering experience, or equivalent demonstrated depth. Strong Python and/or comparable systems/backend programming experience. Strong Linux and software-debugging fundamentals. Experience building or integrating LLM-powered applications, coding agents, or agentic workflows. Experience with APIs, developer tooling, automation, test infrastructure, CI/CD, or large software systems. Ability to understand and modify unfamiliar codebases quickly. Strong engineering judgment around reliability, observability, testing, and maintainability. Excellent written and verbal communication. Demonstrated ability to independently drive ambiguous technical work. Particularly Valuable Experience Claude Code, Claude SDK/API, or similar coding-agent platforms. Agent orchestration, tool use/function calling, MCP, skills, subagents, or multi-step agent workflows. Context engineering, context management, retrieval, memory, or prompt/tool architecture. Automated testing frameworks or AI-generated testing. Large-scale developer infrastructure or internal engineering platforms. Android, wearables, AR/VR, or other device-oriented development environments. CI/CD, build systems, containers, telemetry, or production debugging. Experience embedding AI capabilities into an established engineering ecosystem rather than building standalone demos. Deep ML, computer vision, and model-training expertise are not required. The Engineer Who Will Thrive Here We are looking for someone who combines strong engineering fundamentals with an AI-native way of working . You should be comfortable receiving a problem rather than a detailed implementation plan, investigating the surrounding system, collaborating with stakeholders, and driving toward a working solution. You communicate proactively, surface blockers early, document important decisions, and maintain visibility while working independently. This is a high-autonomy environment where ownership matters as much as technical ability. Example Problems You might be asked to: Integrate an autonomous testing agent with an existing test platform. Improve an agent's ability to understand and navigate a large repository. Build context-management infrastructure for multi-step agent workflows. Automate portions of test creation, execution, debugging, or maintenance. Determine why an agent succeeds on some workflows but fails unpredictably on others. Design tooling that lets an agent safely interact with existing developer infrastructure. Reduce the amount of human intervention required to complete complex engineering workflows. Engagement Long-term consulting engagement working directly with engineers at a major technology company. Remote , with substantial collaboration across engineering teams.