Job Description

Job Summary

Purpose of the role

To design, develop and improve software, utilising various engineering methodologies, that provides business, platform, and technology capabilities for our customers and colleagues.

Key Responsibilities

Accountabilities

• Development and delivery of high-quality software solutions by using industry aligned programming languages, frameworks, and tools. Ensuring that code is scalable, maintainable, and optimized for performance.
• Cross-functional collaboration with product managers, designers, and other engineers to define software requirements, devise solution strategies, and ensure seamless integration and alignment with business objectives.
• Collaboration with peers, participate in code reviews, and promote a culture of code quality and knowledge sharing.
• Stay informed of industry technology trends and innovations and actively contribute to the organization’s technology communities to foster a culture of technical excellence and growth.
• Adherence to secure coding practices to mitigate vulnerabilities, protect sensitive data, and ensure secure software solutions.
• Implementation of effective unit testing practices to ensure proper code design, readability, and reliability.

Skill Requirements

Senior AI Engineer, you should have experience with:

• Expert Python & AI Engineering Frameworks- Deep proficiency in Python and modern AI frameworks (e.g., LangChain, LangGraph, HuggingFace), including vector‑retrieval tooling.
• Agentic AI & Orchestrated Reasoning- Hands‑on experience designing and deploying agentic AI workflows, tool‑using agents, and multi‑step reasoning systems in production environments.
• RAG Architecture & Implementation- Practical experience designing and implementing Retrieval‑Augmented Generation (RAG) solutions, including embeddings, chunking, retrieval optimisation, and safety/guardrails.
• Production‑Grade AI Application Engineering- Proven ability to build and operate full‑stack AI applications (backend, APIs, modern front‑end frameworks such as React) with strong focus on reliability, scalability, security, and observability.
• Cloud‑Native AI Deployment on AWS- Experience deploying AI solutions using AWS services such as Bedrock, SageMaker, Lambda, API Gateway, and vector‑enabled datastores (e.g., OpenSearch, pgvector).

Some other highly valued skills may include:

• End‑to‑End MLOps / LLMOps- Experience with model lifecycle management, evaluation frameworks, monitoring, and CI/CD for AI workloads.
• Technical Leadership & Mentorship- Experience leading junior engineers, driving design reviews, and setting engineering best practices.
• Model Fine‑Tuning Expertise- Understanding of fine‑tuning techniques and when to apply fine‑tuning vs. RAG vs. hybrid strategies.
• Enterprise‑Grade Governance & Security- Experience designing AI systems within regulated or compliance‑heavy environments.

Cost‑Optimised AI Architecture- Ability to design scalable, efficient AI systems through model selection, inference optimisation, and resource‑efficient deployment.

Other Requirements

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