We are looking for a Senior AI-Native Full Stack Engineer — a builder who thinks in systems, ships with purpose, and treats artificial intelligence as a first-class citizen in every layer of the stack. This is not a role for someone who bolts AI onto existing products as an afterthought; we want an engineer who designs with AI from the ground up, embedding intelligence into user experiences, backend logic, and data flows alike.In this role, you will own the full product surface — from pixel-perfect, responsive frontends to robust, scalable backend services — while leveraging cutting-edge AI capabilities including large language models, agentic workflows, and real-time inference to build products that genuinely think. You will collaborate with product, design, and data teams to move fast, iterate boldly, and raise the bar for what modern software can do.

Responsibilities

AI-Native Product Engineering

Design and build product features with AI embedded at the core — not as a feature, but as the foundation
Integrate LLM APIs (e.g. OpenAI, Anthropic, Google Gemini) into user-facing and backend workflows with a focus on reliability, latency, and cost efficiency
Architect and implement agentic systems, prompt orchestration pipelines, and AI-assisted automation flows
Evaluate emerging AI tooling and frameworks (e.g. LangChain, LlamaIndex, Vercel AI SDK) and bring the best into the team's engineering practice
Build retrieval-augmented generation (RAG) systems, embedding pipelines, and vector search integrations to ground AI outputs in real data

Full Stack Development

Build and maintain responsive, performant frontend applications using React, Next.js, or similar modern frameworks
Develop scalable, well-structured backend services and APIs using Node.js, with TypeScript throughout
Design and manage databases across relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis) paradigms
Own the full request lifecycle — from UI interaction through API layer, business logic, and data persistence
Implement real-time features using WebSockets, server-sent events, or streaming APIs

Architecture & Engineering Excellence

Make pragmatic architectural decisions that balance speed of delivery with long-term maintainability
Write clean, well-tested, well-documented code and hold the team to the same standard through code reviews
Design systems that are observable, resilient, and built to scale — with monitoring, alerting, and graceful degradation baked in
Contribute to and evolve engineering standards, patterns, and tooling across the team

Cloud, Infrastructure & DevOps

Deploy and manage applications on cloud platforms (AWS, GCP, or Azure), leveraging serverless, containerised, and managed services as appropriate
Build and maintain CI/CD pipelines that enable rapid, confident delivery
Work with Docker and Kubernetes for containerisation and orchestration
Implement robust security practices including authentication (OAuth 2.0, JWT), authorisation, and secrets management

Collaboration & Leadership

Work closely with product managers and designers to shape features from idea through to shipped
Mentor junior and mid-level engineers, sharing knowledge on both AI-native patterns and full stack fundamentals
Contribute to technical roadmap discussions and help the team make informed build-vs-buy decisions
Communicate technical concepts clearly to non-technical stakeholders

Experience and education

Required

7+ years of professional software engineering experience spanning frontend and backend development
Strong, demonstrable experience building and shipping AI-powered product features using LLM APIs or ML inference services
Deep proficiency in JavaScript and TypeScript, with expert-level knowledge of React and Node.js
Hands-on experience with prompt engineering, context management, and working with the constraints and capabilities of large language models
Proven experience designing and building RESTful and GraphQL APIs
Solid experience with relational and NoSQL databases, including schema design and query optimisation
Demonstrated experience deploying production applications on AWS, GCP, or Azure
Familiarity with vector databases (e.g. Pinecone, Weaviate, pgvector) and semantic search
Experience with containerisation using Docker and CI/CD tooling (GitHub Actions, CircleCI, or similar)

Preferred

Experience building agentic AI systems using frameworks such as LangChain, LangGraph, CrewAI, or AutoGen
Hands-on experience with RAG architectures, embedding models, and document chunking strategies
Familiarity with Databricks or similar data platforms for feeding AI systems with clean, governed data
Experience with real-time streaming or event-driven architectures (Kafka, AWS EventBridge, or similar)
Background in ML fundamentals — understanding model trade-offs, fine-tuning, and evaluation is a strong plus
Experience in a product-led or startup environment where speed and ownership are paramount
Contributions to open-source AI tooling or published writing on AI engineering topics

Education

The following qualifications are accepted for this role:

Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, or a closely related field
Bachelor's degree in a non-technical discipline combined with a recognised software engineering bootcamp and a strong portfolio of AI-native full stack projects
Associate's degree with 9+ years of directly relevant full stack engineering experience, with clear AI-native work evidenced in portfolio
No formal degree with 10+ years of demonstrated senior-level full stack engineering experience, including shipped AI-powered products

Certifications (Advantageous)

AWS Certified Developer – Associate or Professional
Google Professional Cloud Developer or Google Cloud Professional Machine Learning Engineer
Microsoft Certified: Azure Developer Associate
Databricks Certified Machine Learning Associate or Professional
DeepLearning.AI or Coursera AI/ML specialisations relevant to LLMs and generative AI