AI Engineer

We are recruiting a hands‑on AI Engineer to design, build and ope rationalise cloud‑based AI solutions across Microsoft Azure, AWS and Google Cloud. The role sits within the Data & Analytics team and reports into the AI Capability Lead, contributing to enterprise AI delivery primarily in Financial Services (Banking, Insurance, BaaS, Central Bank) with cross‑sector work in Public Sector, Mining and Retail.

The successful candidate combines strong AI / Generative AI engineering with solid data engineering and MLOps foundations. They will deliver production‑grade Generative AI, RAG agents, document intelligence and machine learning solutions on the hyperscalers, integrate them into client systems, and contribute to client engagements, demos, RFPs and the maturing of the firm’s AI capability.

Reports to the AI Capability Lead (Data & Analytics).

Works as part of a multidisciplinary AI delivery team across multiple client business units.

Engages senior stakeholders, Steer Co and (where appropriate) C‑suite, Model Risk and Architecture Boards.

Supports the build‑out of the AI capability: partnerships with Microsoft, AWS, Google, Databricks and Anthropic; pre‑sales support;
PoC and production delivery on cloud AI solutions.

• Design, build and deploy Generative AI and LLM‑based applications, including end‑to‑end RAG agents and agentic / multi‑agent solutions.
• Implement RAG pipelines: chunking strategies, embeddings, dynamic indexing, vector databases, vector indexing, grounding and evaluation.
• Build document intelligence solutions: OCR, classification, custom/neural extraction, table extraction and post‑processing for unstructured data.
• Implement tool/function calling, prompt engineering, fine‑tuning and guardrails for production AI agents.
• Integrate AI models into enterprise systems via APIs, Service Bus, web apps and downstream platforms.
• Experience/knowledge of fine‑tuning generative AI models, MCP, AI tool calling, A2A and graph databases.

• Azure:
  Azure OpenAI, AI Foundry / Prompt Flow, AI Search, Cognitive Services, Document Intelligence, Functions, Container Apps, Web Apps, Synapse, Data Lake, Dev Ops CI/CD.
• AWS:
  Amazon Bedrock (Anthropic/Claude, Titan Embeddings), Lambda, S3 data lakes, Textract and supporting services for AI agents and RAG.
• GCP:
  Vertex AI, Cloud Run, Google App Sheet and supporting services for AI workloads.
• Microsoft Fabric & Power Platform:
  Copilot Studio, AI Builder, Power Apps, Power Automate for rapid AI / automation delivery.
• Databricks: notebooks, ML workflows, Lakehouse and Generative AI capabilities.
• Design and implement cloud AI architectures, including migration patterns across hyperscalers where required.

• Design and implement reliable data pipelines (Python, SQL, PySpark) to support ML and AI workloads.
• Prepare, transform and manage structured and unstructured data for AI use cases (ingestion, ETL/ELT, modelling, lakehouse).
• Implement chunking, embedding, indexing and retrieval mechanisms across vector stores.
• Ensure data quality, lineage and governance alignment, including Purview / catalog tooling where applicable.

• Build CI/CD pipelines for ML and AI models (Azure Dev Ops, Git Hub Actions or equivalent).
• Manage model deployment, monitoring, versioning and performance optimisation.
• Implement scalable, secure inference architectures (Container Apps, Lambda, Cloud Run, Functions).
• Apply Responsible AI, model risk, security and compliance practices (RBAC, Key Vault / Secrets Manager, VNets / Private Endpoints, Monitor / Log Analytics).

• Engage client stakeholders and translate business requirements into AI solution designs.
• Contribute to discovery, design, estimation, costing and commercial models.
• Communicate risks, trade‑offs, model assumptions and limitations clearly to technical and business audiences.
• Produce solution architecture, status reports, Steer Co material, governance artefacts and user documentation.
• Support…