AI Solutions Engineer

Passionate about making a real impact? Be at the forefront of the data centre (DC) industry with a unique focus on sustainability, connectivity and AI which sets us apart as the next generation DC operator. You will also get to gain invaluable experience in a fast-growing industry that is powering the digitalisation wave. Be empowered to co-create the future with our dynamic teams!”We are seeking an AI Solutions Engineer (Agentic AI) to design, build and deploy enterprise AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and AI Agents. You will develop production-ready AI applications that automate knowledge-intensive workflows, integrate with enterprise systems and deliver secure, scalable and trustworthy AI experiences.How You will Make An Impact:AI Solution Development Design, develop and deploy AI agents using LLMs, RAG and prompt engineering.Build scalable AI workflows that automate enterprise business processes.Translate business requirements into practical AI solutions.Develop reusable prompt workflows, tool-calling capabilities and structured outputs.Knowledge & RAG EngineeringBuild and optimise RAG pipelines connected to approved enterprise knowledge sources.Improve retrieval quality through chunking, embeddings, indexing and metadata strategies.Maintain trusted knowledge bases and ensure source-grounded AI responses.AI Platform & IntegrationIntegrate AI applications with enterprise systems, APIs, databases and internal platforms.Develop secure tool-calling capabilities and support deployment into production.Monitor and optimise AI application performance.Model Quality & GovernanceDesign evaluation frameworks to measure response quality, retrieval accuracy and hallucination risks.Optimise prompts, guardrails and model performance.Support governance, version control and human-in-the-loop review processes.Stakeholder CollaborationPartner with product, engineering and business teams to deliver AI solutions.Support demonstrations, UAT, production rollout and technical documentation.Communicate technical concepts clearly to technical and non-technical stakeholders.Skills for Success:Bachelor's Degree in Computer Science, Artificial Intelligence, Data Science or related discipline.3–5 years of software engineering experience with Python.Hands-on experience building LLM applications, AI Agents or RAG solutions.Experience with LangChain, LangGraph, LlamaIndex or similar AI frameworks.Experience integrating APIs, databases and enterprise systems.Knowledge of vector databases, semantic search and prompt engineering.Experience with Git, CI/CD and container technologies.Preferred Skills:Experience with Azure OpenAI, AWS Bedrock or Google Vertex AI.Knowledge of MCP (Model Context Protocol) or AI agent orchestration.Experience deploying open-source LLMs (e.g. vLLM, Ollama).Exposure to MLOps, model fine-tuning or domain adaptation.