IBM
AI Engineer | Public Sector Consulting
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Introduction
A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.
Your Role And Responsibilities
Join IBM Consulting FutureNow's Hybrid Cloud & Data practice and help clients unlock the value of Artificial Intelligence at scale.
We're looking for an experienced AI Engineer to design, build, and deploy enterprise AI solutions across a range of client environments. Working alongside Architects, Data Scientists, Engineers, and client stakeholders, you'll help translate emerging AI capabilities into practical, secure, and production-ready solutions.
This role is particularly well suited to individuals who enjoy solving complex technical challenges through Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic AI systems, and modern integration patterns.
You'll work across a variety of high-profile programmes, helping organisations operationalise AI responsibly while balancing scalability, governance, security, and business outcomes.
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Role & Responsibilities
As a Senior AI Engineer, you will play a key role in the design, development, and deployment of AI-powered solutions across client engagements.
Core Responsibilities
- Design, build, and deploy AI-powered applications and services using Large Language Models and modern AI frameworks.
- Deliver enterprise AI solutions including Retrieval-Augmented Generation (RAG), agentic workflows, semantic search, and knowledge retrieval systems.
- Integrate AI capabilities into existing business platforms, products, and services through APIs and modern integration patterns.
- Collaborate with Architects, Data Scientists, Engineers, and stakeholders to shape scalable and secure AI solutions.
- Evaluate and optimise AI system performance, reliability, and user experience.
- Develop approaches to mitigate hallucinations, improve retrieval quality, and enhance overall model outcomes.
- Contribute to AI governance, evaluation frameworks, security controls, and responsible AI practices.
- Translate business requirements into production-ready technical solutions.
- Support mentoring, knowledge sharing, and AI engineering best practice across delivery teams.
Required Technical And Professional Expertise
- Hands-on experience working with Large Language Models (LLMs) and LLM APIs.
- Experience implementing production RAG (Retrieval-Augmented Generation) solutions and/or agent-based AI systems.
- Strong Python development skills.
- Strong API development and systems integration experience.
- Experience designing and deploying AI-enabled applications in production environments.
- Understanding of AI evaluation techniques, hallucination mitigation, prompt engineering, and responsible AI principles.
- Experience working with unstructured data, embeddings, vector search, or semantic retrieval approaches.
- Strong stakeholder engagement and communication skills.
- Experience delivering solutions from concept through to deployment and adoption.
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This role is subject to pre-employment screening in line with the UK Government's Baseline Personnel Security Standard (BPSS). Additional National Security Vetting (NSV) requirements may apply, including eligibility for Security Check (SC) or Developed Vetting (DV).
Preferred Technical And Professional Experience
- Experience with frameworks such as LangChain, LangGraph, DSPy, Semantic Kernel, LlamaIndex, or similar.
- Experience with vector databases such as Qdrant, Pinecone, Weaviate, FAISS, or equivalent.
- Familiarity with agent orchestration, tool calling, and multi-agent architectures.
- Experience deploying AI solutions within cloud environments (AWS, Azure, or GCP).
- Exposure to AI governance, model assurance, or regulated industries.
- Experience operating within Public Sector, Defence, Financial Services, or other high-assurance environments.
- Familiarity with CI/CD, containerisation, and modern software engineering practices.
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