AI Engineer

Role Context & Reporting Line:

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.