Engineering Team Lead - AI Innovation (Remote, Contract)

• We will review your application against our job requirements. We do not employ machine learning technologies during this phase as we believe every human deserves attention from another human. We do not think machines can evaluate your application quite like our seasoned recruiting professionals—every person is unique. We promise to give your candidacy a fair and detailed assessment.
• We may then invite you to submit a video interview for the review of the hiring manager. This video interview is often followed by a test or short project that allows us to determine whether you will be a good fit for the team.
• At this point, we will invite you to interview with our hiring manager and/or the interview team. Please note: We do not conduct interviews via text message, Telegram, etc. and we never hire anyone into our organization without having met you face-to-face (or via Zoom). You will be invited to come to a live meeting or Zoom, where you will meet our INFUSE team.
• From there on, it’s decision time! If you are still excited to join INFUSE and we like you as much, we will have a conversation about your offer. We do not make offers without giving you the opportunity to speak with us live. 
  
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We're starting a new engineering team that runs like a small product company inside the company. It picks up real problems and ideas, decides what's worth building, ships fast, and owns what it ships. Sometimes that's an internal automation that saves a team hours every week. Sometimes it's a brand-new product built from an idea. You'd lead this team and build alongside it.

We're hiring a strong engineer first. This is a hands-on role: you'll own projects end to end, from scoping and architecture through development to production. No-code and workflow tools have their place here, but the core of the job is designing and writing production software.

Our stack: Python and TypeScript/Node.js, SQL, AWS, Claude for LLM work. We work AI-native: agentic coding tools are part of how engineering gets done here.

• →Own the architecture of what the team builds: service boundaries, data flows, integrations, failure modes.
• →Build alongside the team: Python and TypeScript services, SQL against live databases, LLM-based agents where they earn their place.
• →Turn new product ideas into working prototypes fast, and take the ones that prove out to production. Part of the job is killing weak ideas early and saying why.
• →Take incoming automation requests, analyze the process behind each one, and prioritize by financial impact. Some requests deserve a week of engineering. Some deserve a "no". You make that call and explain it to the requester.
• →Use AI coding tools daily. Claude Code is our default; deep experience with a comparable agentic tool also works. What matters is that you drive the tool, verify its output and catch its mistakes.
• →Ship to non-technical users and stay with each product until people actually use it.
• →Lead a team of engineers: set direction, review code, unblock, grow them, build AI-native engineering culture

• A senior software engineering background. You've designed and shipped production systems and made the architecture calls that come with them.
• End-to-end delivery. You've taken projects from scoping through architecture and development to production more than once, and owned them after launch.
• Solid Node/Python and SQL; You can read and integrate with code in stacks you didn't choose.
• Real experience building with LLMs and agents in production: at least one shipped LLM-powered feature or agent, and you can talk concretely about evals, prompt versioning, cost control and failure design.
• An AI-native way of working: agentic coding tools (Claude Code, Codex, Cursor or similar) as your daily driver, and the judgment to verify what they produce.
• Experience leading engineers. You've run a small team: set direction, reviewed code, unblocked people and helped them grow.
• Product thinking and ownership. You can take a messy problem, put a number on what it costs, decide what to build first, and stay responsible for the result after it ships.
• Clear communication with non-engineering stakeholders, in English.

• Experience taking a product from zero to first users.
• Event-driven and workflow-engine experience (Kafka, Temporal or similar).
• Experience replacing no-code or workflow-tool automations with production code.