Machine Learning Manager (AI)

Role Summary

We are seeking an AI & Machine Learning Manager with strong hands-on expertise in AI/ML engineering, Generative AI, data science and enterprise solution delivery. The role requires a techno-functional leader who can engage business stakeholders, identify high-value AI opportunities, shape solution architecture and guide engineering teams through development, deployment and adoption.

The candidate must combine sound technical judgement with business understanding. They will lead the end-to-end delivery of secure, scalable and production-ready AI solutions, while remaining close enough to the technology to review designs, challenge implementation choices and guide teams on engineering standards.

Key Responsibilities

AI Strategy & Business Alignment

• Work with business leaders and domain teams to identify, assess and prioritise AI/ML opportunities based on business value, feasibility, risk and expected return.

• Translate business problems into clear AI use cases, functional requirements, solution options, delivery plans and measurable success criteria.

• Define AI roadmaps and support business cases, proof-of-concept assessments and the transition of successful pilots into production solutions.

AI/ML Solution Design & Delivery

• Lead the design, development, testing and deployment of machine learning, deep learning, NLP, predictive analytics and Generative AI solutions.

• Guide the development of LLM-based applications, Retrieval-Augmented Generation (RAG), AI agents, conversational systems and intelligent automation solutions.

• Provide hands-on technical direction across the AI lifecycle, including data preparation, feature engineering, model selection, evaluation, integration and production release.

• Review solution designs, model approaches, code quality and deployment plans; resolve technical issues and guide teams on sound engineering practices.

Architecture, Platforms & MLOps

• Define and govern scalable AI solution architectures across cloud and enterprise platforms, with attention to performance, reliability, security and cost.

• Establish MLOps practices for model versioning, experiment tracking, CI/CD, deployment, monitoring, drift detection, retraining and lifecycle management.

• Promote reusable components, model-serving patterns, prompt templates, evaluation frameworks and AI accelerators to improve delivery quality and speed.

• Work with data engineering, cloud, application, security and infrastructure teams to ensure reliable integration with enterprise systems and data platforms.

Techno-Functional & Stakeholder Leadership

• Act as the bridge between business stakeholders and technical teams, ensuring that business needs are reflected in solution design and delivery.

• Lead discovery sessions, requirement workshops, solution walkthroughs, demonstrations and design discussions with business and technology stakeholders.

• Explain technical options, trade-offs, limitations, risks and expected outcomes in clear business terms, including to senior management.

• Drive user adoption, change management and the integration of AI solutions into business processes.

Team Leadership & Delivery Management

• Lead, mentor and develop AI engineers, ML engineers, data scientists and solution architects; set clear technical and delivery expectations.

• Own delivery planning, estimation, prioritisation, sprint execution, dependencies, risks and issue resolution across AI initiatives.

• Track delivery milestones, model quality, system performance, user adoption and business impact; drive corrective action where required.

• Build a culture of technical ownership, experimentation, documentation, knowledge sharing and continuous improvement.

AI Governance, Risk & Responsible AI

• Embed responsible AI principles, privacy, security, explainability, fairness and human oversight into AI solution design and delivery.

• Establish controls for model validation, data usage, access, auditability, monitoring and change management in line with enterprise policies and applicable regulations.

• Work with risk, legal, compliance and information security teams to assess AI risks and support governance reviews before and after deployment.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering or a related discipline.

• Substantial professional experience in AI/ML engineering, data science, Generative AI or enterprise technology delivery, including experience leading technical teams or complex initiatives.

• Strong hands-on knowledge of machine learning, deep learning, NLP, predictive modelling and Generative AI, with the ability to guide implementation decisions.

• Strong programming proficiency in Python and practical experience with relevant AI/ML libraries and frameworks such as PyTorch, TensorFlow or scikit-learn.

• Practical experience designing and delivering LLM-based applications, including prompt engineering, RAG, embeddings, vector search and AI agent patterns.

• Working knowledge of model evaluation, validation, deployment, monitoring, model drift and lifecycle management.

• Experience with MLOps concepts and tools for experiment tracking, model registry, CI/CD, deployment and production monitoring.

• Ability to elicit and analyse business requirements, define functional and non-functional needs, and convert them into technical designs and delivery plans.

• Experience working with senior stakeholders and cross-functional teams, managing expectations, explaining technical trade-offs and presenting solution recommendations.

• Strong problem-solving, communication, documentation, delivery management and team leadership skills.

Preferred Qualifications & Experience

• Experience with Microsoft Azure AI Services, Azure OpenAI, Azure Machine Learning, Azure AI Foundry, Databricks or Microsoft Fabric; equivalent AWS or Google Cloud experience is also relevant.

• Hands-on exposure to LangChain, LlamaIndex, Semantic Kernel, vector databases and agentic AI frameworks.

• Experience with MLflow, Azure ML, Kubeflow, Databricks Model Serving or comparable MLOps platforms.

• Experience delivering enterprise solutions in BFSI, insurance, healthcare, retail, manufacturing or other regulated environments.

• Knowledge of Responsible AI frameworks, model risk management, explainability, fairness testing and AI governance.

• Relevant cloud, AI engineering, machine learning or Databricks certifications.

Core Technology Landscape

Python | PyTorch | TensorFlow | scikit-learn | NLP | Deep Learning | Predictive Analytics | Generative AI | LLMs | Azure OpenAI | Azure AI Foundry | Azure Machine Learning | Databricks | MLflow | RAG | Embeddings | Vector Databases | LangChain | LlamaIndex | Semantic Kernel | AI Agents | Prompt Engineering | MLOps | Model Serving | Git | CI/CD | Azure DevOps | Cloud Platforms | Power BI | Microsoft Fabric