Senior AI Engineer - M&G plc.

At M&G, our purpose is to give everyone real confidence to put their money to work. With a heritage dating back more than 175 years, we have a long history of innovation in savings and investments, combining asset management and insurance expertise to offer a wide range of solutions.

Our two distinct operating segments, Asset Management and Life, work together to provide access to balanced, long-term investment and savings solutions.

Through telling it like it is, owning it now and moving it forward together with care and integrity; we are creating an exceptional place to work for exceptional talent.

We will consider flexible working arrangements for any of our roles and offer workplace adjustments to ensure you have the support you need to succeed in your role.

Role Overview

As a Senior AI Engineer within the M&G Life Technology team, you will play a leading role in the design, development, deployment, and optimisation of enterprise AI solutions that deliver measurable business outcomes. Working closely with Product Owners, Architects, Data Engineers, Security teams, and Business stakeholders, you will help shape the future of AI adoption across M&G by building scalable, secure, and responsible AI capabilities.

We are seeking experienced AI Engineering professionals to join the team on a 12-month fixed term basis to support us through a period of exciting business and technological growth. Successful candidates will combine strong software engineering expertise with deep knowledge of artificial intelligence, machine learning, and generative AI technologies. The role requires a product-led mindset, ensuring that solutions are technically robust, aligned to business value, and designed for operation within a regulated financial services environment.

As a member of the Technology team, you will contribute to experimentation, innovation, engineering excellence, and the development of enterprise AI capabilities that improve customer outcomes, operational efficiency, decision-making, and developer productivity.

Key Responsibilities

AI Solution Delivery

• Partner with the AI Product Owner during discovery activities to assess feasibility, shape solution options, and contribute to product roadmaps and prioritisation.
• Translate business challenges into scalable technical solutions through MVPs, proofs of concept, and production-ready implementations.
• Contribute to product vision, user stories, acceptance criteria, and technical architecture decisions.
• Support experimentation and rapid innovation while maintaining engineering quality and governance standards.

AI Engineering & Generative AI

• Design, develop, deploy, and optimise AI-powered applications, APIs, copilots, agents, and intelligent automation solutions.
• Develop Retrieval-Augmented Generation (RAG) architectures, prompt engineering frameworks, vector search solutions, and enterprise knowledge retrieval capabilities.
• Integrate Large Language Models (LLMs) into enterprise applications, business processes, and engineering workflows.
• Build and maintain agentic AI solutions using orchestration frameworks such as LangChain, Semantic Kernel, and equivalent technologies.
• Evaluate emerging AI technologies and recommend appropriate adoption strategies.
• Implement evaluation frameworks and guardrails to measure model quality, safety, reliability, and business effectiveness.

Software Engineering & Platform Delivery

• Apply modern software engineering principles including automated testing, source control, CI/CD, infrastructure-as-code, observability, resilience, and security‑by‑design.
• Build scalable cloud-native applications, APIs, microservices, and data pipelines using modern engineering frameworks and patterns.
• Collaborate with platform engineering teams to enable AI capabilities across enterprise platforms and developer ecosystems.
• Develop reusable frameworks, libraries, patterns, and standards to accelerate AI adoption across engineering teams.
• Contribute to AI‑assisted software development practices and developer productivity initiatives.

MLOps, Monitoring & Production Operations

• Implement MLOps practices supporting model lifecycle management, deployment automation, testing, monitoring, and continuous improvement.
• Build and maintain observability, telemetry, and analytics capabilities for AI solutions.
• Monitor model performance, usage patterns, and business outcomes using defined KPIs and OKRs.
• Implement model evaluation, drift detection, performance monitoring, and AI safety controls.
• Investigate and resolve production issues to ensure reliability, resilience, and operational effectiveness.

Data Governance, Security & Responsible AI

• Collaborate with Data Engineering teams to prepare, manage, and govern high-quality datasets for AI solutions.
• Ensure all AI solutions comply with enterprise security, governance, privacy, risk, regulatory, and responsible AI requirements.
• Support explainability, auditability, lineage, and transparency requirements for AI systems.
• Develop and implement AI guardrails and governance controls throughout the full AI lifecycle.
• Work with data governance and cataloguing platforms such as Microsoft Purview, Databricks Unity Catalog, or equivalent technologies.

Business Value & Adoption

• Build AI solutions that deliver measurable business outcomes and support KPI and OKR definition and tracking.
• Develop dashboards, measurement frameworks, and reporting mechanisms to quantify business value and adoption.
• Support training, user enablement, go‑live activities, and change adoption initiatives.
• Gather user feedback and continuously improve AI products through iterative enhancement.

Collaboration, Leadership & Capability Development

• Partner with Product Owners, Architects, Business Analysts, Data Engineers, Risk teams, and operational stakeholders to solve complex business challenges.
• Provide technical leadership and mentoring to engineers adopting AI capabilities.
• Contribute to AI communities of practice, engineering standards, and capability development initiatives.
• Promote innovation, experimentation, and continuous improvement across engineering teams.
• Share knowledge, best practices, and lessons learned to

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