AI Engineer

We are recruiting a hands-on AI Engineer to design, build and operationalise 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.

Role Context & Reporting Line:

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, SteerCo 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.

Key Responsibilities:

AI & Generative AI Engineering

• 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.

Cloud AI Solution Delivery Proficient in any of the following (At least 1 CSP) (Azure | AWS | GCP)

• Azure: Azure OpenAI, AI Foundry / Prompt Flow, AI Search, Cognitive Services, Document Intelligence, Functions, Container Apps, Web Apps, Synapse, Data Lake, DevOps 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 AppSheet 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.

Data Engineering for AI (AI-Data Engineering)

• 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.

AIOps & Operationalisation

• Build CI/CD pipelines for ML and AI models (Azure DevOps, GitHub 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).

Consulting & Delivery

• 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, SteerCo material, governance artefacts and user documentation.
• Support pre-sales, demos, PoCs and RFP responses; contribute to the AI capability roadmap and uplift of junior engineers.

Required Skills & Experience:

• Degree in Computer Science, Data Science, Engineering, Mathematics or a related quantitative field.
• 3+ years' experience delivering AI / ML / data solutions, ideally in a consulting or enterprise delivery environment.
• 1-2+ years' hands-on Generative AI engineering experience (LLMs, RAG, embeddings, vector DBs, prompt engineering).
• 3+ years' broader ML / AI delivery experience (supervised ML, feature engineering, evaluation, NLP).
• Strong data engineering: pipelines, Python / PySpark, data modelling, lakehouse patterns.
• Cloud experience on at least one of Azure, AWS or GCP, with working knowledge of a second; containerisation and CI/CD.
• Experience integrating AI into enterprise systems via APIs, web apps and messaging.
• Business acumen: ability to link AI solutions to business value, ROI and risk.
• Strong communication, stakeholder management, collaboration and analytical skills.

Advantageous Certifications in Any of the following:

Certifications - AWS (AI / ML & Architecture)

• AWS Certified AI Practitioner.
• AWS Certified Machine Learning - Specialty.
• AWS Certified Machine Learning Engineer - Associate.
• AWS Certified Solutions Architect (Associate or Professional).
• AWS Certified Data Engineer - Associate.

Certifications - Microsoft Azure (AI & Data)

• Microsoft Certified: Azure AI Engineer Associate (AI-102).
• Microsoft Certified: Azure AI Fundamentals (AI-900).
• Microsoft Certified: Azure Data Scientist Associate (DP-100).
• Microsoft Certified: Fabric Analytics Engineer Associate (DP-600) or Fabric Data Engineer Associate (DP-700).
• Microsoft Certified: Azure Data Engineer Associate (DP-203).
• Microsoft Certified: Azure Solutions Architect Expert (AZ-305).
• Microsoft Applied Skills credentials in Generative AI, Azure OpenAI, Semantic Kernel, Copilot, AI Builder or Document Intelligence.

Certifications - Google Cloud (AI & Data)

• Google Cloud Certified - Professional Machine Learning Engineer.
• Google Cloud Certified - Generative AI Leader.
• Google Cloud Certified - Professional Data Engineer.
• Google Cloud Certified - Professional Cloud Architect.
• Google Cloud Certified - Cloud Digital Leader.

Certifications - Other AI / Data Platforms

• Databricks Certified Generative AI Engineer Associate.
• Databricks Certified Machine Learning Associate / Professional.
• Databricks Lakehouse Fundamentals / Data Engineer.
• Anthropic / Claude developer credentials.
• NVIDIA Deep Learning Institute (DLI) certifications in Generative AI or LLMs.
• Harvard or other recognised Data Science / Machine Learning credentials.

Other Advantageous Experience

• Microsoft Fabric, Azure AI Foundry, Azure OpenAI and solution delivery experience.
• AWS Bedrock with Anthropic Claude, Titan Embeddings and Textract in production.
• GCP Vertex AI and Cloud Run delivery experience.
• Knowledge graphs, advanced RAG patterns, agent orchestration and multi-agent frameworks.
• Exposure to Model Risk Management (MRM), Architecture Review Boards and Responsible AI frameworks.
• Experience productising AI solutions and contributing to AI CoE / Target Operating Model design.
• Track record in pre-sales, RFPs, technical demos and client workshops.

Success Measures:

• Production-grade AI solutions deployed across Azure, AWS and / or GCP.
• Scalable, governed data and AI pipelines established and reused across engagements.
• Measurable contribution to revenue, pre-sales and RFP wins.
• Reduced time-to-production for new AI use cases through reusable patterns and accelerators.
• Demonstrable mentorship of junior engineers and uplift of the broader AI capability.
• High-quality stakeholder engagement, SteerCo and executive communication.

Please Note:

As all iqbusiness roles require honesty in the handling of or access to cash, finances, financial systems, or confidential information; our recruitment process requires that the following background checks be completed: credit, criminal, ID, and qualification verification.

iqbusiness is committed to sustainable growth and transformation, we embrace diversity and employ previously disadvantaged individuals.