About M&G India
We are M&G India, the strategic innovation and digital hub for M&G. Established in 2003, we have offices in Mumbai and Pune.
Our teams work closely with colleagues across the Group worldwide to drive transformation, build digital capability, and support sustainable growth. By leveraging technology, AI, automation, and process excellence, we bring new ways of thinking to improve outcomes for both customers and colleagues.
Grounded in a vibrant culture with strong foundations, we are central to how M&G is transforming as a business.
About 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.
At M&G we are on a very exciting digital transformation journey towards a more agile, customer-centric and digital-first way of working. We have lots to improve and plenty of interesting challenges we'd like you to get involved in.
We are on a journey to reinvent how people think about their financial future. Making conversations around retirement easier and more relevant, offering diversified products, rethinking and improving the end-to-end experience for our customers and using technology to effectively engage with them in real time and help them make better decisions about their finances.
The journey has started already, are you ready to join us? We need the best digital minds onboard in order to achieve our goal. We are looking for an experienced and innovative AI Engineer to design, build, deploy, and optimise AI-powered solutions within our regulated financial services environment. The role will be based out of our Engineering Hub in India and working in close collaboration with our UK colleagues across engineering, architecture, data, security, and business teams to deliver scalable AI capabilities that improve customer outcomes, operational efficiency, developer productivity, and decision-making.
We promote a supportive, diversified, international environment with an engineering culture we're proud of. We encourage personal growth and invest in open-minded engineers keen on learning, who continuously try to make things better.
You should apply if
- You have strong software engineering fundamentals in Python (or similar languages) and hands-on experience building production-grade AI solutions.
- You want to take a key role on our teams and help us deliver impactful, responsible AI capabilities into a regulated environment.
- You have hands-on expertise in generative AI – LLMs, prompt engineering, embeddings, vector search and Retrieval-Augmented Generation (RAG) pipelines.
- You understand Agile practices like Scrum, Kanban and DevSecOps and have practical experience of at least one.
- You enjoy collaborating closely with cross-functional teams – product, architecture, data, risk and business – to translate problems into AI solutions.
- You care deeply about security, observability, governance, and responsible AI controls.
- You want to actively engage with our internal AI community of practice on defining standards, frameworks and engineering excellence.
- You love learning and enjoy evaluating emerging AI technologies as well as exploring areas you might not have experience with yet.
- You are versatile when finding solutions and good at explaining complex AI concepts to both technical and non-technical audiences.
- You are proactive and a team player – within your feature team and the wider engineering community – reviewing PRs, mentoring others and collaborating to create the best outcome.
Accountabilities/Responsibilities
- AI Solution Delivery: Ensure the timely and accurate delivery of AI and machine learning solutions built on modern cloud-native architectures, meeting both external and internal quality standards.
- Code & Model Quality: Maintain high standards of code and model quality through code reviews, automated testing, evaluation harnesses, and adherence to engineering best practices.
- System Performance & Observability: Monitor and optimise the performance, scalability, resilience, and observability of AI-powered applications, including model monitoring, drift detection and auditability.
- Documentation: Create and maintain comprehensive documentation for AI solutions, including architecture diagrams, prompt patterns, API documentation and responsible AI controls.
- Compliance, Security & Responsible AI: Ensure AI solutions comply with enterprise governance, security, risk, and regulatory requirements, including data privacy, explainability and operational resilience standards.
- Innovation: Stay updated with the latest generative AI, LLM, and cloud AI advancements to evaluate emerging technologies and recommend appropriate adoption strategies.
- Stakeholder Satisfaction: Ensure high levels of stakeholder satisfaction by delivering AI solutions that meet or exceed business expectations and drive measurable outcomes.
Responsibilities
- :AI Solution Engineering: Design, develop, and deploy AI and machine learning solutions – including AI-powered applications, APIs, copilots, agents, and automation workflows – using modern cloud-native architectures.
- RAG & Vector Search: Develop and optimise Retrieval-Augmented Generation (RAG) pipelines, prompt engineering patterns, embeddings, and vector search implementations, and integrate large language models (LLMs) into enterprise applications and engineering workflows.
- Software Engineering & Platform Delivery: Apply strong engineering principles including CI/CD, automated testing, observability, resilience, and infrastructure-as-code; build scalable and secure microservices and data pipelines using modern engineering frameworks.
- Data & Governance: Collaborate with data engineering teams to prepare, manage and govern high-quality datasets for AI solutions, and work with data cataloguing and governance platforms such as Microsoft Purview, Databricks Unity Catalog, or equivalent.
- Collaboration: Partner with product owners, architects, business analysts, risk teams and operational stakeholders to translate business problems into AI solutions, supporting experimentation, prototyping and innovation initiatives.
- Troubleshooting: Diagnose and resolve technical issues across AI pipelines, model performance, and integration points, providing timely support and fixes.
- Testing & Evaluation: Develop and execute unit, integration, and model evaluation tests (including offline/online evaluation of LLM outputs) to ensure the reliability and quality of delivered solutions.
- Mentorship & Technical Leadership: Provide technical leadership and mentoring to engineering teams adopting AI capabilities, and contribute to internal AI communities of practice, engineering standards, and capability development.
- Reusable Frameworks: Contribute to reusable AI frameworks, libraries, prompt and agent patterns, and engineering standards that accelerate adoption across the organisation.
- Continuous Improvement: Proactively identify areas for improvement in the AI delivery lifecycle, recommending and implementing enhancements to optimise efficiency, quality and responsible adoption.
Key Stakeholder Management
Internal
Product Owners
Head of Product Engineering
Head of Studio
Tech Leads
Delivery Leads
External
N/A
Knowledge, Skills, Experience & Educational Qualification
Knowledge & Skills
- Strong software engineering experience in Python (Java or similar languages also welcome) with a focus on building production-grade AI solutions.
- Hands-on experience with machine learning and generative AI frameworks such as Azure OpenAI, LangChain, Semantic Kernel, Hugging Face, and PyTorch or TensorFlow.
- Strong understanding of prompt engineering, embeddings, vector databases and RAG / vector search architectures, with experience developing AI agents, copilots or conversational AI solutions.
- Experience with cloud platforms, preferably Microsoft Azure, and knowledge of containerisation and orchestration technologies such as Docker and Kubernetes.
- Experience with CI/CD pipelines – Azure DevOps or equivalent tooling – and strong understanding of API development and integration patterns.
- Hands-on experience with observability, MLOps and AI monitoring tooling to support model performance, drift detection, auditability and explainability.
- Working knowledge of data cataloguing and governance platforms such as Microsoft Purview, Databricks Unity Catalog, or equivalent.
- Sonarqube and Checkmarx (or equivalent) for static code analysis and security scanning.
- Able to use team collaboration tools – Teams, Jira, Confluence, etc.
Experience
- Proven experience delivering quality AI and software solutions in enterprise-scale environments. Strong Python, generative AI, RAG/vector search, and cloud (preferably Azure) skills are a Must Have for this role; additional relevant skills are desirable.
- Experience acting as a subject matter expert on generative AI, LLMs, AI frameworks and AI engineering best practices.
- Experience working within regulated environments (such as banking, insurance, pensions or asset management) with strong governance, security, operational resilience, auditability and risk control requirements.
- DevSecOps engineering experience to transform, optimise and integrate AI delivery processes, including automated CI/CD pipelines, observability, and infrastructure-as-code.
- Experience working in fast paced agile environments and understanding of agile/scrum project planning, execution and risk management.
- Solid knowledge of design patterns, software architecture and refactoring concepts as applied to AI systems.
- Practical expertise in performance tuning and optimisation of AI pipelines, including latency, cost, and quality trade-offs in LLM-based systems.
- Stay current on industry trends and best practices in generative AI, Responsible AI frameworks and AI governance.
- Desirable: exposure to Power Platform, Microsoft Copilot Studio, or enterprise AI enablement platforms, and AI-assisted SDLC practices and developer productivity tooling.
- Ability to come up with creative solutions for ambiguous problems and learn quickly.
- Experience contributing to, and achieving, continuous improvement goals.
- Working with third party suppliers and vendor partners.
Educational Qualification:
A Bachelor’s degree in any discipline
M&G Behaviours relevant to all roles
Tell It Like It Is – respectfully speaking up to create better ways forward – both direct and empathetic.
Own It Now – putting your name on things with confidence to drive progress and results quickly.
Move It Forward Together – forming cross-functional teams to seize the right opportunities and solve real problems.
Always with care and integrity.
- Take personal accountability, collaborate with colleagues, make effective decisions and help others operate at their best.
- Operate in a fair, trustworthy, and genuine way with my colleagues and customers.
- Give and receive feedback, be self-aware and be driven to learn and develop.
- Understand customer needs and adapt to the changing environment.
Don't worry if you don't know everything, we're happy to train people on the technologies you'll need in the job....
