Tech Lead

Make the call on how things are structured, scaled, and maintained - and defend it to both engineers and stakeholders.\nDrive the AI engineering layer - LLM integration, agent design, prompt strategy, and the tradeoffs between model capability, latency, and cost.\nBridge 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.\nOwn delivery. Sprint planning, prioritisation, cross-team dependencies.

You keep things moving without burning people out.\nStay hands-on where it matters - code reviews, architecture docs, and debugging sessions when something goes wrong in production.\nOwn infra and DevOps decisions alongside the team - multi-env deployments, async worker architecture, scaling strategy, and CI/CD pipelines.\nDevelop your engineers. 1-on-1s, clear feedback, growth paths. You make people better.\n\nMust have\n12–15 years hands-on engineering - real production systems, end to end\nStrong full-stack depth - credible architecture decisions on both backend and frontend\n7–8 years managing engineering teams including BE, FE, and DevOps\nHands-on AI/LLM product experience - shipped real features, understands failure modes, can scope AI work realistically\nExperience with async task and worker architectures - queues, background jobs, distributed workloads\nComfortable with Python backend systems at production scale\nCloud infra ownership - containers, multi-environment deployments, scaling, and secrets management\nStrong communicator - clear in writing, direct in feedback, calm under pressure\n\nGood to have\nExperience with LLM agent orchestration frameworks\nLLM observability and cost management - tracing, latency profiling, model cost optimisation\nAWS at scale - compute, queuing, caching, and monitoring\nIaC experience - infrastructure as code at production scale\nBackground in developer tools, builder platforms, or AI-native products\nStartup or early-stage experience - making good decisions fast with imperfect information\n\nStack - hands-on experience expected Python · FastAPI · PostgreSQL · Redis · Celery · LLM APIs · LangChain / LangGraph · Docker · AWS · CI/CD\n\nWhat good looks like here\nYou don't pick sides between quality and velocity - you figure out how to have both, and you've done it before.\nYour team ships with confidence because they know what they're building, why it matters, and what done looks like.\nProduct trusts you - not just to execute, but to push back when something isn't technically sound or scoped correctly.\nYou 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.\nThe engineers on your team are better at their jobs six months after working with you.\n\nWhat this is not\nA pure people manager role - if you've been fully out of technical decisions for years, this isn't the right fit.\nA role where you wait for perfect specs - you'll drive clarity, not receive it.\nA big-company Tech Lead role - no large support structure, you'll move fast, make calls, and own outcomes.