Role Overview :

As Engineering Lead, you will lead small, high-performing engineering teams embedded with clients. You will make delivery predictable by shielding engineers from noise, giving stakeholders the right information and holding the quality bar without becoming the bottleneck. The role demands equal craft in people leadership and software engineering.

Key Responsibilities :

- Review architecture decisions, challenge assumptions and mentor senior engineers.

- Set standards for code quality, testing, observability, security and AI-specific practices.

- Review cloud architecture, environment strategy and release pipelines across AWS, Azure or GCP.

- Own the delivery plan for one or more engagements, covering scope, milestones, dependencies and risk.

- Run well-structured sprints with reliable commitments and lean process.

- Spot blockers early, remove them before they become incidents and escalate clearly.

- Manage AI delivery risks such as model latency, output variability, evaluation loops and data dependencies.

- Scope AI features realistically, with validation and fallback strategies.

- Track the AI engineering landscape and turn it into resourcing and tooling decisions.

- Act as the primary engineering contact for client product managers, directors and tech leads.

- Explain engineering complexity in business terms : timelines, trade-offs and confidence levels.

- Negotiate scope with the team's capacity and quality standards in view.

- Lead 1on1s, structured feedback and growth conversations with clarity and care.

- Make code quality, intellectual honesty and continuous improvement the team default.

- Protect engineers' focus from context-switching and scope creep.

Requirements :

- 8+ years in software engineering, including 1+ years leading engineering teams in product or consulting environments.

- Hands-on depth : contributing code, leading complex system design and running rigorous code reviews.

- Track record of leading teams on modern stacks : React and TypeScript frontends, Node.js or Python (FastAPI, Django or Flask) backends, and relational and document databases.

- End-to-end delivery experience, from backlog to production, with consistent quality and predictability.

- Working knowledge of the AI stack (LLM APIs, RAG, embeddings, vector databases, agent frameworks, LLM gateways).

- Working knowledge of cloud (AWS, Azure or GCP), containerisation and CI/CD, enough to challenge design and release decisions.

- Direct, specific feedback grounded in observable behaviour.

- Comfort with consulting accountability : multiple engagements, client scrutiny and a high professional bar.

- Sound judgement on when to push back and when to adapt.