Agentic Engineer — Build, Experiment & Ship

About the Role Agentic Engineer II to design, develop, and deploy rapid prototypes of AI-driven tools and agentic workflows that automate complex engineering tasks and dramatically improve efficiency. You’ll work in a fast-paced, hybrid environment to accelerate cross-functional engineering teams by delivering working code and measurable efficiency gains.

What You'll Do

• Ideate, architect, and build proof-of-concepts (PoCs) for AI-powered engineering tools, multi-agent systems, and automation scripts.
• Partner with Product Engineering teams to understand workflows, identify bottlenecks, and embed AI solutions where they matter most.
• Leverage LLMs, vector databases, and agentic frameworks to build autonomous workflows capable of reasoning, tool-use, and decision-making.
• Refine and automate multi-disciplinary engineering pipelines within Product Engineering.
• Run rapid experiments: hypothesis → prototype → deploy → evaluate → iterate.
• Embrace a fail-fast approach — short cycles, quick learning, and moving on with better information.
• Containerize and self-host solutions — taking full ownership from development through deployment.
• Evaluate tools and models through hands-on use, not benchmarks alone.
• Identify what scales and what doesn’t based on real-world deployment behavior.
• Document validated patterns that contribute to the team’s shared AI use case library.
• Think in terms of scale: share work, knowledge, and practices with others so they can join the mission.

What We're Looking For

• Minimum 2+ years of experience with AI & agentic frameworks, hands-on with LLMs and orchestration frameworks like LangChain, LlamaIndex, CrewAI, or similar.
• Proficiency in Python or TypeScript (or similar) for fast development and AI integration.
• APIs & Integrations: REST APIs, webhooks, and SDKs; experience integrating AI agents with GitHub, Jira, Slack, or internal tools.
• Data Foundations: familiarity with RAG, vector embeddings, vector databases, and prompt engineering techniques.
• Workflow Automation Architecture: n8n, LangGraph, CrewAI, ChromaDB, and Docker.
• Ability to make complex and analytical decisions and recommendations to management.
• Strong problem-solving and analytical skills; excellent oral & written communication; strong interpersonal skills; conflict resolution & risk escalation.
• Experience with the product development process and understanding of technical & commercial aspects.
• Education: Master’s degree in Computer Engineering, Computer Science, Electrical Engineering, Artificial Intelligence, Information Technology, Software Engineering, Data Science, Data Analytics, Information Systems, Information Science, or Machine Learning.

Nice to Have

• Navigate ambiguity to make decisions and deliver completed work, taking initiative with partial information and iterating as insights evolve.
• Curiosity and self-direction — build things because you want to understand how they work and have shipped real projects.
• You’ve containerized, self-hosted, and deployed real projects; something running somewhere outside your own machine.
• You stay current with AI tools by using them, not just reading about them; share a repo, live URL, or walkthrough and discuss learnings from what didn’t go as planned.