Agentic Engineer - Build, Experiment & ShipAbout the Role:Are you fascinated by the power of AI agents to transform how engineering teams work? Do you thrive in a high-velocity environment where you can build, test, and iterate on wild ideas in days rather than months?
We are looking for
AI Agentic Development Engineers to join and build a new Applied AI team. In this role, you won't just be maintaining legacy systems, you will be the ultimate accelerator for our cross-functional engineering teams. Your mission is to design, develop, and deploy rapid prototypes of AI-driven tools and agentic workflows that automate complex tasks, eliminate friction, and radically optimize our engineering processes.
We operate on a philosophy of
learning fast, failing fast, and delivering faster. If you are a bias-for-action developer who thinks outside the box and measures success by working code and measurable efficiency gains, we want you to be on our team.
This is an individual contributor role and the selected candidate will have the opportunity to work a hybrid schedule.
Key ResponsibilitiesAs an
Agentic Engineer II, you will be responsible to:
- Ideate, architect, and build proof-of-concepts (PoCs) for AI-powered engineering tools, multi-agent systems, and automation scripts.
- Partner closely with Product Engineering teams to deeply understand their workflows, identify bottlenecks, and embed AI solutions where they matter most.
- Leverage LLMs, vector databases, and agentic frameworks (e.g., LangChain, CrewAI, AutoGen) 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 12 prototype 12 deploy 12 evaluate 12 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: how do I share my work, knowledge, and practices with others so they can join the mission
Qualifications- Minimum of 2+ years of experience in the following:
- AI & Agentic Frameworks: Hands-on experience working with LLMs (OpenAI, Anthropic, open-source models) and orchestration frameworks like LangChain, LlamaIndex, CrewAI, or similar.
- Programming Mastery: Proficiency in Python, TypeScript, or similar languages optimized for fast development and AI integration.
- APIs & Integrations: Strong experience connecting disparate systems via REST APIs, webhooks, and SDKs (e.g., integrating AI agents with GitHub, Jira, Slack, or internal engineering tools).
- Data Foundations: Familiarity with RAG, vector embeddings, vector databases (e.g., Pinecone, Chroma, Milvus), 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/analytical skills, where problems are complex
- Excellent oral & written communication skills
- Strong interpersonal skills
- Conflict resolution & risk escalation skills
- Excellent leadership skills in a team environment
- Experience with the product development process
- Understanding of technical & commercial aspects
Additional Things We're Looking For- Navigate ambiguity to make decisions and deliver completed work, maintaining momentum even when conditions are not fully defined.
- Take initiative to begin work with partial information, iterating and refining requirements as insights evolve.
- You're naturally curious and self-directed - you build things because you want to understand how they work, not just because it's on a task list.
- You've containerized, self-hosted, and deployed real projects. You've seen something work perfectly in a local environment and behave differently in production - and that experience has made you a better engineer.
- The clearest signal: you have something running somewhere that isn't your own machine. You stay current with AI tools by using them, not just reading about them.
- We're more interested in what you've built than what's on your resume. Share something you've deployed - a repository, a live URL, or a brief walkthrough. Tell us what didn't go as planned and what you took away from it.
Education Requirements- Masters 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 required
