Engineering Manager

About Job

We're hiring a Tech Lead Manager to own a slice of Business Acceleration & AI — architecture, delivery and people — for a team of 3–5 engineers. This is a high-velocity builder's seat: you and your team go from a stakeholder's problem to a working, AI-powered tool in days, using AI coding agents and LLM-native patterns as your default way of building, not a side experiment. You report to an Engineering Manager / Senior EM and act as the technical and people anchor for your pod.

Skills & Qualification

• 8–10 years of software engineering experience, with real ownership of shipping products end-to-end

• Significant, recent hands-on experience building with AI/LLM technology — agents, RAG, LLM APIs, AI coding tools — not occasional Copilot use, but AI-powered tools or products you've actually shipped

• Proven ability to operate hands-on while managing people — this is not a seat for someone who wants to step back from the code

• Direct experience managing or leading a small engineering team, formally or as a tech lead with real people responsibilities

• A demonstrated bias for speed — a track record of shipping 0-to-1 tools or products in days or weeks, not quarters

• Strong product instincts — comfortable scoping ambiguous, under-specified problems directly with business stakeholders

• Judgment on where AI tooling helps and where it doesn't — able to articulate a real boundary, not a slogan

• Comfortable operating in a fast-moving, less-finished environment than a mature platform — this is a builder's seat, not a maintainer's

Responsibilities

• Own architecture and technical direction for rapid internal tools and AI-native product builds

• Stay hands-on: personally build using AI coding agents and LLM-native patterns, going from idea to working product in days — setting the pace and quality bar by doing, not by review comment

• Manage, coach and grow 3–5 engineers — 1:1s, performance conversations, hiring, and levelling calls

• Partner directly with business, ops, risk and GTM stakeholders to scope ambiguous problems fast, without waiting on a full PRD

• Make fast build-vs-buy-vs-prompt calls — know when a thin AI wrapper beats a custom build, and when it doesn't

• Set and evolve how the team uses AI coding agents, LLM APIs and automation platforms to multiply output

• Own reliability and iteration speed for what you ship — these are internal-facing tools, but business-critical ones, so breakages have real cost

• Bring judgment on where AI tooling should replace manual work entirely versus augment it