Tech Lead

The role

We're looking for a senior Tech Lead who combines deep hands-on experience with the ability to lead a team and drive delivery. You'll own the day-to-day execution of our engineering function - managing a cross-functional team of backend, frontend, DevOps engineers, and Product team while staying close enough to the code to make good architectural calls. You're the person the team turns to when things are complex and the person who translates product intent into technical reality.

What you'll do

• Lead and grow a cross-functional team - backend, frontend, and DevOps - setting the pace and the standard for how the team works.
• Own system architecture across the full stack. Make the call on how things are structured, scaled, and maintained - and defend it to both engineers and stakeholders.
• Drive the AI engineering layer - LLM integration, agent design, prompt strategy, and the tradeoffs between model capability, latency, and cost.
• Bridge engineering and product. Challenge scope when needed, make sure what ships actually solves the problem, and absorb ambiguity so your team doesn't have to.
• Own delivery. Sprint planning, prioritisation, cross-team dependencies. You keep things moving without burning people out.
• Stay hands-on where it matters - code reviews, architecture docs, and debugging sessions when something goes wrong in production.
• Own infra and DevOps decisions alongside the team - multi-env deployments, async worker architecture, scaling strategy, and CI/CD pipelines.
• Develop your engineers. 1-on-1s, clear feedback, growth paths. You make people better.

Must have

• 12–15 years hands-on engineering - real production systems, end to end
• Strong full-stack depth - credible architecture decisions on both backend and frontend
• 7–8 years managing engineering teams including BE, FE, and DevOps
• Hands-on AI/LLM product experience - shipped real features, understands failure modes, can scope AI work realistically
• Experience with async task and worker architectures - queues, background jobs, distributed workloads
• Comfortable with Python backend systems at production scale
• Cloud infra ownership - containers, multi-environment deployments, scaling, and secrets management
• Strong communicator - clear in writing, direct in feedback, calm under pressure

Good to have

• Experience with LLM agent orchestration frameworks
• LLM observability and cost management - tracing, latency profiling, model cost optimisation
• AWS at scale - compute, queuing, caching, and monitoring
• IaC experience - infrastructure as code at production scale
• Background in developer tools, builder platforms, or AI-native products
• Startup or early-stage experience - making good decisions fast with imperfect information

Stack - hands-on experience expected Python · FastAPI · PostgreSQL · Redis · Celery · LLM APIs · LangChain / LangGraph · Docker · AWS · CI/CD

What good looks like here

• You don't pick sides between quality and velocity - you figure out how to have both, and you've done it before.
• Your team ships with confidence because they know what they're building, why it matters, and what done looks like.
• Product trusts you - not just to execute, but to push back when something isn't technically sound or scoped correctly.
• You can walk into a system you didn't build, understand it quickly, and make sensible decisions about where to invest and where to leave it alone.
• The engineers on your team are better at their jobs six months after working with you.

What this is not

• A pure people manager role - if you've been fully out of technical decisions for years, this isn't the right fit.
• A role where you wait for perfect specs - you'll drive clarity, not receive it.
• A big-company Tech Lead role - no large support structure, you'll move fast, make calls, and own outcomes.