This role combines strong full stack engineering expertise withhands-on fluency in AI-assisted development tools todesign, build, and delivermodern digital solutions at speed and scale. You'llwork across the entireapplication stack - from intuitive user interfaces torobust APIs and dataservices - while leveraging AI coding assistants and,where appropriate,integrating AI capabilities into the products you build. Youwill help the teamraise engineering productivity and quality through smart,responsible use of AI,and contribute to shaping how to delivers AI-enabled software.
Key Responsibilities
Full Stack Application Development
Design, develop, test, and deploy end-to-end web applications spanning frontend, backend services, APIs, and data layers
Translate functional and non-functional requirements into well-architected, maintainable software components using established design patterns
Build and maintain microservices and RESTful / GraphQL APIs using modern stacks such as Java/Spring Boot, .NET, Python, or Node.js
Develop responsive, accessible user interfaces using modern JavaScript/TypeScript frameworks (e.g., React, Angular, Vue)
Model data and work with both SQL and NoSQL databases design efficient queries, schemas, and integration patterns
Contribute to architecture discussions and trade-off analyses for performance, scalability, security, and cost
AI-Assisted Software Engineering
Use AI-assisted coding tools (e.g., GitHub Copilot, Cursor, Claude Code, Amazon Q Developer, Gemini Code Assist, JetBrains AI Assistant, or equivalent) as a daily part of the development workflow
Leverage AI tools to accelerate coding, refactoring, code review, unit test generation, test data creation, and documentation
Apply prompt engineering and context-design techniques to get high-quality, trustworthy outputs from AI coding assistants
Critically review AI-generated code for correctness, security, performance, licensing, and alignment with project and coding standards before committing
Measure and communicate the productivity and quality impact of AI-assisted workflows on project delivery
AI Solution Integration & Delivery
Integrate AI and Generative AI capabilities into enterprise applications - including LLM-powered features, Retrieval-Augmented Generation (RAG), chatbots and virtual assistants, intelligent document processing, recommendations, and agentic workflows
Build against foundation model APIs and managed AI services such as Azure OpenAI, Amazon Bedrock, Google Vertex AI / Gemini, Anthropic Claude, and OpenAI
Work with vector databases and AI application frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel pgvector, Pinecone, Weaviate, FAISS) to deliver context-aware experiences
Design AI features with clear evaluation criteria, guardrails, and human-in-the-loop checkpoints
Partner with data scientists, AI/ML engineers, and solution architects to take AI capabilities from prototype to production
Contribute to pre-sales and client engagements by prototyping AI-enabled features and shaping practical, value-driven solution designs
Quality Engineering & Secure Coding
Follow secure coding principles and security guidelines to prevent common vulnerabilities across frontend, backend, and AI integrations
Write and maintain unit, integration, and end-to-end tests meet project and organisation test coverage targets
Perform static code analysis, code reviews, and threat-aware reviews of AI-generated code and AI-integrated features
Address defects, performance issues, and production incidents through disciplined root-cause analysis
DevOps & Continuous Delivery
Adopt Agile, DevOps, and CI/CD practices to deliver software iteratively and reliably
Build and maintain pipelines (e.g., Jenkins, GitLab CI) for automated build, test, and deployment
Containerise applications and deploy to container platforms (Docker, Kubernetes) and cloud environments (AWS, Azure, GCP)
Instrument applications for observability - logs, metrics, traces, and, where applicable, AI-feature evaluation telemetry
Collaboration & Knowledge Sharing
Partner with business analysts, designers, data scientists, and product owners to translate user needs into working software
Participate in and lead peer reviews, design reviews, and knowledge-sharing sessions
Mentor junior developers on full stack engineering fundamentals and effective, responsible use of AI tools
Document designs, APIs, and AI-integration patterns in a clear and reusable way
Responsible AI & Engineering Excellence
Champion responsible use of AI tools, including IP protection, client data handling, confidentiality, and licence-compliance considerations
Help define and uphold team guardrails and standards for AI-assisted development and AI-integrated products
Stay current with the rapidly evolving AI tooling, model, and framework landscape, and bring relevant advances back to the team
Contribute to assets, reusable components, and reference implementations that accelerate future AI-enabled delivery
Job Requirements
Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Technology and related field
3 to 8 years of professional experience building and delivering production web applications as a full stack developer
Strong hands-on proficiency in at least one backend stack: Node.js (Express, NestJS), Golang, Python.
Strong hands-on proficiency in at least one modern frontend framework: React (Next.js a plus), Angular, or Vue.js, with solid fundamentals in JavaScript/TypeScript, HTML5, and CSS/CSS3
Solid understanding of API design (REST, JSON GraphQL a plus), microservices, event-driven patterns, and integration with third-party systems
Experience with SQL and NoSQL databases (e.g., Oracle, MS SQL Server, PostgreSQL, MySQL, MongoDB, Redis)
Experience with Git-based source control, Agile delivery, test-driven development, and CI/CD toolchains
Working experience with containers and cloud platforms (Docker, Kubernetes AWS, Azure, or GCP)
Demonstrated day-to-day use of AI-assisted coding tools (e.g., GitHub Copilot, Cursor, Claude Code, Amazon Q Developer, Gemini Code Assist, JetBrains AI Assistant, Tabnine, Windsurf, or equivalent) and ability to articulate how these tools have improved your delivery quality and speed
Working knowledge of prompt engineering and how to structure effective context and instructions for AI coding assistants
Familiarity with Large Language Models and Generative AI concepts - tokens, context windows, embeddings, vector search, and Retrieval-Augmented Generation (RAG)
Exposure to at least one LLM / GenAI platform or SDK (OpenAI, Azure OpenAI, Anthropic Claude, Amazon Bedrock, Google Vertex AI / Gemini, Hugging Face)
Involvement in at least one project that delivered an AI solution capability - such as a GenAI / LLM-powered application, RAG system, chatbot or virtual assistant, intelligent document processing, ML-powered feature, or AI agent - is a strong plus
Exposure to AI application frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel), vector databases (e.g., Pinecone, Weaviate, pgvector, FAISS), agentic patterns, tool/function calling, Model Context Protocol (MCP), and AI evaluation or guardrail practices is a plus
Awareness of responsible AI principles, data privacy, IP considerations, and security implications of using AI tools on client engagements
Strong problem-solving, analytical thinking, and sound judgement in when - and when not - to rely on AI-generated output
Self-motivated, customer-focused, and committed to high engineering and delivery standards