Lead AI/ML Consultant

Job Title: Lead AI/ML Consultant

Job Location: London, UK/ Hybrid

Job Type: FTE

We are seeking a highly experienced AI Consultant to design, implement, and drive enterprise-grade AI solutions on the Azure platform Microsoft AI Foundry. This role combines hands-on experience, solution architecture, and technical leadership, with responsibility for guiding teams and proactively supporting multiple accounts and initiatives.

The candidate needs to have deep insurance domain knowledge, a strong grip on modern AI architecture patterns, and the communication skills to operate comfortably in both technical and domain jargons.

Key Responsibilities

Deep understanding of the insurance domain: underwriting, policy administration, claims, reinsurance, and distribution.

Ability to speak credibly to business stakeholders about business impact without needing a translator

Familiarity with insurance data types: structured (policy, claims, exposure) and unstructured (loss reports, survey documents, correspondence).

Experience applying Responsible AI frameworks in practice: bias assessment, fairness metrics, explainability techniques and model cards.

Demonstrable hands-on experience with Microsoft Azure AI Foundry: project setup, model catalogue, evaluation frameworks, and deployment pipelines.

Proficiency with Azure OpenAI Service: GPT model deployment, fine-tuning, content filtering, and token management.

Experience with Azure AI Search (vector and hybrid search), Azure AI Document Intelligence, and Azure Machine Learning.

Ability to wire these services together into coherent, production-ready architectures not just run individual demos.

Understanding of Azure security and identity controls as they apply to AI workloads: managed identities, private endpoints, and data residency.

Develop and deploy RAG-based knowledge systems using Azure AI Search, Azure OpenAI Service, and relevant orchestration frameworks (LangChain, Semantic Kernel, or Prompt Flow).

Design and implement AI agent architectures for orchestrating multi-step insurance workflows (e.g. automated claims triage, document extraction pipelines, underwriting assist tools).

Build and evaluate ML / predictive models relevant to insurance: churn, risk scoring, fraud detection, reserve estimation.

Validate outputs from engineering teams against the original solution design; identify deviations before they reach the client.

Establish and apply Responsible AI practices: bias evaluation, explainability, model monitoring, and governance documentation aligned to FCA and industry expectations.

Define reusable AI solution accelerators, reference architectures, and best-practice playbooks for the insurance vertical.

Ability to produce governance documentation that satisfies both technical and regulatory audiences.

Mentor and provide technical direction to junior consultants and engineers on the team.

Required Skills & Qualifications

Python proficiency sufficient to build prototypes, review engineering output, and debug issues independently.

Familiarity with orchestration frameworks: LangChain, Semantic Kernel, or Microsoft Prompt Flow.

Hands-on experience with Microsoft Azure AI Foundry

Working knowledge of Azure DevOps or GitHub for managing AI experiment tracking and deployment pipelines.

Ability to read and interpret SQL; experience with cloud data platforms (Databricks, Synapse, or equivalent) is advantageous.

Preferred Qualifications/Certifications

Microsoft Certifications: AI-102 (Azure AI Engineer), DP-100 (Azure Data Scientist), AI-900, or equivalent.

Familiarity with insurance-specific AI regulation guidance.

Exposure to ML Ops practices: model versioning, drift detection, retraining pipelines, and A/B evaluation in production.

Experience with multi-modal AI (document + image pipelines relevant to property claims or risk surveys).

Familiarity with data bricks

Prior experience in a vendor or consultancy environment managing multiple concurrent client engagements.

Knowledge of CI/CD for Databricks (Azure DevOps, GitHub Actions).

Exposure to data governance, security, and compliance frameworks.