- Rapidly acquire domain expertise through direct observation, translate between business and engineering seamlessly, and mentor engineers in your area on immersion.

- Influence senior stakeholders effectively, manage complex stakeholder landscapes with competing agendas, and build trust rapidly with new stakeholders.

Delivery :

- Lead rapid delivery initiatives across teams in your area, coach on prototype-first approaches, and establish trust through consistent fast delivery.

- Build complete applications rapidly across any technology stack, select the right tools for each problem, and define clear criteria for prototype-to-production transitions.

Generative AI :

- Architect RAG systems for complex use cases across teams, implement advanced techniques (hybrid search, reranking, query expansion), mentor engineers on RAG best practices, and establish RAG standards.

- Lead evaluation strategy across teams, establishing annotation guidelines, training human-calibrated LLM judges, and building evaluation pipelines that connect tracing to datasets to experiments.

People :

- Build high-performing teams across your area, navigate complex interpersonal dynamics, foster psychological safety, and create environments where diverse perspectives are valued.

- Influence through communication at all levels - from frontline to executive.

- Handle difficult conversations skilfully and train engineers in your area on effective communication.

AI-Augmented Development :

Optimise AI tool usage across teams in your area, train engineers on AI-augmented and agentic engineering workflows, evaluate new AI development tools, and establish practices that balance AI speed with verification rigour.

Scale :

- Design complex multi-component systems end-to-end, evaluate architectural options for large initiatives across teams, guide technical decisions for your area, and mentor engineers on architecture.

- Create debt reduction strategies across teams, influence roadmap decisions to include debt work, and teach engineers when to accept debt for speed versus when to invest in quality.

Documentation :

- Define documentation standards across teams in your area, create documentation systems and templates, train engineers on spec-driven development, and ensure documentation quality across projects.

- Lead pattern generalization initiatives, defining criteria for when to generalize versus keep custom.

Reliability :

- Define reliability standards across teams in your area, drive post-incident improvements systematically, design capacity planning processes, and mentor engineers on SRE practices.

Requirements :

- Must have 7+ years of relevant professional software engineering experience, with demonstrated full-stack delivery across backend and frontend.

- Must have deep production experience with Python AND JavaScript/TypeScript, working comfortably across the full stack.

- Must have hands-on experience architecting production generative AI applications - LLM integrations, vector databases, RAG systems, and evaluation pipelines

- Must have strong experience with modern frontend frameworks (Next.js / React) and backend API development.

- Must have extensive experience with cloud platforms (AWS preferred; Azure/GCP valued), including infrastructure-as-code (CloudFormation / Terraform).

- Must have working knowledge of multiple database paradigms - relational (PostgreSQL), document, and key-value (Redis) - with ability to select the right storage per problem.

- Mandatory (Experience 7) - Must have strong experience with CI/CD pipelines (e.g. GitHub Actions), containerization, and production deployment strategies.

- Must have demonstrable fluency with AI coding tools (Claude Code, Cursor, GitHub Copilot, or similar) and proven ability to design agentic engineering workflows and train teams on them

- Preferred (Experience) - Advanced RAG techniques - hybrid search, reranking, query expansion - and establishing RAG standards across teams