Full Stack Gen AI Developer

Job Description

We are looking for a Staff Software Engineer who will design and build scalable full-stack software systems that deliver measurable business value.

The role involves developing AI-powered applications, including production RAG systems, LLM integrations, and optimized retrieval pipelines.

You will identify repeatable patterns in your work and collaborate with platform teams to generalize solutions into reusable frameworks.

This role also requires leading technical initiatives, mentoring engineers, and turning complex business challenges into reliable, production-ready systems.

What You’ll Do

Responsibilities

Business

Immerse in operations until you think like an insider

Rapidly acquire domain expertise through direct observation

Translate between business and engineering seamlessly

Mentor engineers in your area on immersion

Influence senior stakeholders effectively

Manage complex stakeholder landscapes with competing agendas

Build trust rapidly with new stakeholders

Delivery

Lead rapid delivery initiatives across teams in your area

Coach on prototype-first approaches

Establish trust through consistent fast delivery

Build complete applications rapidly across any technology stack

Select the right tools for each problem

Define clear criteria for prototype-to-production transitions

Generative AI

Architect RAG systems for complex use cases across teams

Implement advanced techniques such as hybrid search, reranking, and query expansion

Mentor engineers on RAG best practices

Establish RAG standards

Lead evaluation strategy across teams

Establish annotation guidelines

Train human-calibrated LLM judges

Build evaluation pipelines that connect tracing to datasets to experiments

People

Build high-performing teams across your area

Navigate complex interpersonal dynamics

Foster psychological safety

Create environments where diverse perspectives are valued

Influence through communication at all levels — from frontline to executive

Handle difficult conversations skillfully

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

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

Mentor engineers on architecture

Create debt reduction strategies across teams

Influence roadmap decisions to include debt work

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

Ensure documentation quality across projects

Lead pattern generalization initiatives

Define 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

Mentor engineers on SRE practices

Process

Lead lean transformations across teams in your area

Design flow-optimised processes

Coach engineers on lean principles

Balance speed with sustainability

Establish metrics that drive improvement

Role Behaviours

Own the Outcome: Drive accountability culture focused on outcomes, not deliverables. Own business relationships and impact metrics across your function. Make trade-offs between custom solutions and generalisable work. There is no “I must run this by X.” Ensure verification rigour for AI-generated code.

Be Polymath Oriented: Champion cross-disciplinary learning. Create holistic solutions spanning technical and business domains. Embody the Renaissance Engineer ideal. Translate specialised knowledge into accessible explanations. Think like a business insider.

Communicate with Precision: Create spec-driven development practices. Mentor others on precise communication. Span C-level executives to frontline workers. Drive clarity as a core value across your function. Represent the organisation externally.

Don’t Lose Your Curiosity: Drive team curiosity through challenging questions. Create environments where exploration and experimentation are encouraged. Model problem discovery orientation. Seek out ambiguity rather than avoiding it.

Think in Systems: Shape systems design practices across your function. Conduct chaos engineering experiments. Influence cross-team architecture decisions. Create clarity from complexity. Bridge technical systems with business processes.

Practitioner-level Skills

Architecture & Design

Code Quality & Review

Full-Stack Development

Problem Discovery

Rapid Prototyping & Validation

Retrieval Augmentation

AI-Augmented Development

Multi-Audience Communication

Business Immersion

Stakeholder Management

Team Collaboration

Working-level Skills

DevOps & CI/CD

Cloud Platforms

AI Evaluation & Observability

Technical Debt Management

Data Integration

Site Reliability Engineering

Service Management

Foundational-level Skills

AI Literacy

Data Modelling

Technical Writing

Pattern Generalization

Knowledge Management

Developer Experience

What You Bring

Bachelor’s degree in computer science, Software Engineering, or related field with 7+ years of relevant professional experience

Deep production experience with Python and JavaScript/TypeScript across backend and frontend

Strong experience with modern frontend frameworks such as Next.js or React

Strong backend API development experience

Extensive experience with cloud platforms (AWS preferred; Azure or GCP also valued)

Experience with infrastructure-as-code tools such as CloudFormation or Terraform

Deep working knowledge of multiple database paradigms including:

relational databases (PostgreSQL)

document databases

key-value stores (Redis)

Strong experience with CI/CD pipelines

Experience with GitHub Actions

Containerisation and production deployment strategies

Demonstrable fluency with AI coding tools such as:

Claude Code

Cursor

GitHub Copilot

Hands-on experience architecting production generative AI applications including:

LLM integrations

vector databases

RAG systems

evaluation pipelines

Experience leading technical initiatives across multiple teams

Experience mentoring engineers

Experience establishing engineering practices

Experience navigating ambiguous problem spaces

Experience working directly with business stakeholders and end users

Experience shipping working solutions rapidly

Experience in an embedded, forward-deployed, or consulting-style engineering model is a strong plus