Ai engineer
Summary
Design and deploy AI/ML and Generative AI solutions, build RAG pipelines, and maintain scalable data/AI pipelines in cloud environments like Azure/AWS/GCP.
We are seeking a highly skilled AI Engineer with strong data engineering foundations to design, build, and operationalise AI solutions within modern cloud environments. This role focuses on implementing scalable AI systems, ensuring robust data pipelines, and enabling production‑grade machine learning and Generative AI solutions.
The ideal candidate combines deep technical execution capability with practical experience in data platforms, MLOps, and AI application development. This role plays a critical part in translating AI architecture into working, scalable solutions that deliver measurable business value.
Key Responsibilities: AI & ML / Generative AI Engineering Build and deploy ML models and Generative AI/LLM‑based applications. Develop RAG pipelines including chunking, embedding, indexing, retrieval. Implement AI‑powered automation workflows. Integrate AI models into enterprise systems. Data Engineering for AI Design and maintain data pipelines for ML workloads. Prepare and manage structured and unstructured data. Develop ingestion, modelling, feature engineering. Ensure data quality, lineage, governance. MLOps & Operationalisation Build CI/CD for ML/AI. Manage deployment, monitoring, versioning. Implement scalable inference architectures. Apply Responsible AI and compliance. Cloud & Platform Engineering Deliver AI workloads on Azure/AWS/GCP. Use containerisation, serverless, APIs. Apply Ia C and optimise cost/performance. Consulting & Delivery Engage stakeholders, translate requirements. Contribute to discovery, design, estimation. Communicate risks and trade‑offs. Produce documentation and governance artefacts. Skills & Experience: Degree in CS/Data/Engineering. 3+ years consulting. Certifications advantageous. AI/LLM engineering: 1–2 years Gen AI. 3+ years ML/AI delivery. RAG, embeddings, vector DBs, prompts. Data engineering: pipelines, SQL, modelling, lakehouse. 7+ years in data/engineering. Cloud: Azure ML, AWS Sagemaker, containerisation, CI/CD. Business acumen: link AI to business value, ROI. Soft skills: communication, collaboration, analytical. Microsoft Fabric, Azure AI Foundry, Azure Open AI. Knowledge graphs, integration patterns, multi‑agent. Advanced RAG, agent orchestration. MRM exposure, productising AI solutions. Success Measures: Production‑grade AI solutions deployed. Scalable data/AI pipelines established. Contribution to revenue and pre‑sales. Reduced time‑to‑production. Mentorship and capability uplift.