Senior AI Engineer, M&G

• Strong hands-on experience in Machine Learning, Generative AI, Large Language Models (LLMs), Agentic AI, and Decision Intelligence systems
• Experience designing and implementing:
• Retrieval-Augmented Generation (RAG)
• Prompt engineering frameworks
• AI agents and copilots
• Vector databases and embeddings
• Enterprise search and knowledge retrieval systems
• Strong understanding of model evaluation, model selection, guardrail design, responsible AI, and AI governance
• Strong software engineering experience using Python, Java, C#, or similar languages
• Experience building production-grade applications, APIs, and cloud-native services
• Experience applying automated testing, CI/CD, infrastructure-as-code, and DevSecOps practices
• Knowledge of containerisation and orchestration technologies such as Docker and Kubernetes
• Understanding of enterprise integration patterns and API-first architectures
• Experience delivering solutions within regulated environments such as insurance, pensions, investments, banking, or asset management
• Understanding of governance, operational resilience, security, auditability, risk management, and data privacy requirements
• Experience building solutions that meet regulatory and responsible AI expectations
• Behavioural Competencies
• Strong analytical and problem-solving capability
• Excellent communication and stakeholder management skills
• Ability to explain complex technical concepts to both technical and non-technical audiences
• Product- and outcome-oriented mindset focused on delivering measurable value
• Collaborative approach with experience working in agile, cross-functional teams
• Passion for innovation, continuous learning, and engineering excellence

Desirable

• 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
• 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 foster organisational AI capability growth
• What Success Looks Like
• Delivery of secure, scalable, and measurable AI solutions that achieve business outcomes
• Successful deployment of AI capabilities into production with high levels of reliability, observability, and governance
• Increased organisational productivity through responsible AI adoption
• Demonstrable business value measured through defined KPIs and OKRs
• Strong compliance with governance, privacy, security, and regulatory standards
• Growth of enterprise AI capability through mentoring, reusable assets, and technical leadership