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Technical Lead
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The Role
You will set the technical pace of the team while remaining close to the code: turning product goals into pragmatic designs, helping engineers make good decisions, and carrying important work through to reliable production outcomes.
As part of a lean, high-impact engineering organisation, this is not an architecture-only or meeting-led role. You will work alongside the Director of Engineering, product colleagues, and engineers to balance delivery, quality, operability, and longer-term technical health. You will lead through clarity, example, and influence rather than line management.
LLMs are part of how the team creates leverage. You will use them thoughtfully to accelerate research, design exploration, implementation, tests, debugging, and documentation, while remaining accountable for the result: protecting sensitive information, checking assumptions, reviewing generated code, and sharing repeatable practices that improve the whole team's effectiveness.
What This Person Will Do
- Provide hands-on technical leadership from discovery and design through implementation, release, and production validation.
- Translate product and business goals into clear technical approaches, making trade-offs visible and keeping solutions proportionate to the problem.
- Design, build, and review software across the.NET, TypeScript, and Python estate, stepping into complex or high-risk work when the team needs support.
- Set and reinforce practical engineering standards for architecture, testing, security, observability, documentation, and maintainability.
- Improve delivery flow by removing technical blockers, breaking work into sensible increments, and ensuring releases have clear evidence of success.
- Mentor engineers through pairing, design reviews, and constructive code review, helping the team broaden ownership and reduce key-person dependencies.
- Work closely with platform software engineers on reliability, CI/CD, GitOps, infrastructure automation, and operational improvements.
- Help diagnose difficult production and pre-production issues, turning incidents and recurring friction into durable improvements and useful runbooks.
- Champion effective use of LLMs and coding agents for planning, implementation, testing, investigation, and documentation, while ensuring outputs are reviewed, secure, and technically sound.
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What Success Looks Like
- The team has clearer technical direction and makes good day-to-day decisions without unnecessary escalation.
- Important work reaches production in small, safe increments with clear evidence that it works.
- Engineering standards improve, recurring operational problems reduce, and ownership spreads across the team.
- LLM-assisted working saves meaningful time while maintaining security, quality, and human accountability.
Ideal Candidate
Essential
- Hands-on leadership: Strong experience leading the technical delivery of production software while remaining a regular, credible contributor to the codebase.
- System design: Sound judgement across APIs, distributed services, data flows, and cloud-native applications.
- Languages: Commercial experience with more than one of.NET, TypeScript/Node.js, and Python, plus the ability to work comfortably across an unfamiliar codebase.
- Cloud and delivery: Practical knowledge of AWS, Kubernetes, and modern delivery practices, including CI/CD, GitOps, observability, and safe change management.
- Pragmatism: A track record of simplifying systems, making balanced trade-offs, and improving reliability without letting platform work become detached from customer value.
- Communication: Clear with engineers, product colleagues, and non-technical stakeholders, including the ability to explain uncertainty, risk, and options.
- Leadership style: Collaborative, decisive when needed, open to challenge, and focused on helping others make better decisions.
- AI-assisted engineering: Confident, responsible use of LLMs or coding agents to increase personal and team throughput, with disciplined validation of generated work.
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Nice to Have
- Experience building AI-enabled product features, RAG systems, evaluation workflows, or production LLM integrations.
- Experience with high-volume ingestion, queues, search, embeddings, or vector databases.
- Knowledge of infrastructure as code, cloud cost optimisation, security controls, or platform engineering at scale.
- Experience in a small SaaS or data-product team where engineers share ownership from product discovery through production operation.
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