You will be taken to Marsh McLennan’s own careers page to apply. Job ID R_366607.
Job description
Marsh is seeking a talented individual to join our IT team as an AI Native Intern. This role is based in Pune. This is a hybrid role with a requirement of working at least three days a week in the office.
Designation: Engineer
IT application team is an in-house application development and support team for a suite of applications used by the Insurance Broking & Employee Benefit business
We will count on you to
Collaborate with cross-functional teams to design, build, integrate, test, and support high-quality AI-enabled solutions that deliver practical business outcomes.
Harness AI-powered developer tools – including coding assistants such as GitHub Copilot, Claude, and Gemini – and integrate LLM APIs as active accelerators across your day-to-day development workflow, not as an afterthought.
Apply prompt design and structured interaction techniques to obtain reliable, contextual outputs from generative AI systems for practical engineering and business use cases.
Verify and validate AI-assisted outputs for correctness, relevance, logic, security, privacy, performance, and fitness for purpose before they are used in production or decision-making.
Participate in the full lifecycle of AI-enabled solution delivery, including problem framing, design, integration, testing, deployment, monitoring, and support.
Document solution assumptions, design decisions, guardrails, validation approach, and technical rationale for AI-enabled features and workflows, and support business process improvement initiatives.
Learn and apply best practices in software engineering, Agile/Scrum, and DevOps within an AI-augmented team.
Adapt quickly to a fast-changing AI-enabled project environment, demonstrating resilience and a growth mindset.
What you need to have
B.E. / B. Tech degree (Computer Science, Information Technology, or related engineering discipline).
Minimum 60% aggregate in Graduation and Post Graduation.
Will Graduate in 2027 with no active backlogs in any subject.
Hands-on exposure to at least one modern programming language – Python, JavaScript, Java, C#, or equivalent.
Strong understanding of AI/ML and generative AI concepts, including LLM behavior, prompting, retrieval, evaluation, limitations, and common failure modes.
Working knowledge of SQL and/or NoSQL databases
Demonstrated practical application of AI in at least one project, prototype, workflow, or use case beyond code generation.
What makes you stand out
Learning Agility – you pick up new tools, patterns, and frameworks fast and apply them independently.
Critical Thinking – you interrogate AI outputs rather than accepting them at face value; you ask "why" and "so what?"
Adaptability – you thrive in ambiguous, rapidly changing environments where the best practice of today may be outdated by next quarter.
Analytical Thinking – you break complex problems into structured components and reason from data and evidence.
Collaboration – you work effectively across engineering, product, and business teams.
Digital Literacy – you are comfortable navigating new digital tools and ecosystems with minimal handholding.
Innovation Mindset – you actively explore new ways to do things better, faster, or with greater impact using AI.
Prompt Engineering – ability to write, refine, and iterate effective prompts for generative AI tools to produce reliable, contextual outputs.
LLM Foundations – understanding of how generative AI models work, their probabilistic nature, limitations, and failure modes.
Code Verification & Quality – disciplined practice of reviewing and validating AI-generated code, not just accepting it.
Security Hygiene – awareness of security risks in AI-assisted development, including prompt injection, data leakage, and compliance.
Business Context – ability to align AI outputs with real business objectives and assess return on investment.
Modern Tech Stacks – exposure to API development, front-end frameworks, server-side programming, DevOps tooling.
Availability – openness to work in a 24x7 shift environment when required.
Why join our team
Work at the frontier – you will be part of a team actively integrating AI into real enterprise software, not just experimenting in a sandbox.
Professional development – we invest in your growth through structured learning, mentoring, and access to Marsh McLennan's global development programmes.
Inclusive culture – we foster a vibrant, diverse workplace where different perspectives drive better solutions for colleagues, clients, and communities.
Scale and impact – with operations in 130 countries and 95,000+ colleagues, the systems you contribute to have real global reach.
Career pathways – AI Native Interns have direct visibility to specialist AI roles, engineering tracks, and cross-functional rotations across the Marsh McLennan enterprise.
What is an AI Native Intern? An AI Native Intern is an engineering professional who understands how to use AI effectively and responsibly to solve practical business and engineering problems, while applying enterprise guardrails, validation, and human judgment.
At Marsh, this role sits at the Engineering tier of the AI Agility pyramid: professionals who combine strong software development fundamentals with the cognitive readiness and applied AI skills needed to build, integrate, and critically evaluate AI-powered solutions in a live enterprise environment. What distinguishes an AI Native Intern is the judgment to know when AI should be used, when it should not, how to validate its outputs, and how to align it to business outcomes, quality, safety, and compliance requirements. We are looking for individuals who are measurably ready for AI-enabled work today who can translate emerging AI capabilities into practical, scalable, and well-governed solutions, and who possess the learning agility to keep pace as technology continues to evolve.
To broaden specific asks: Document solution assumptions, design decisions, guardrails, validation approach, and technical rationale for AI-enabled features and workflows
..and support Business process improvement initiatives
Additional requirement suggestion for stricter filtering: Demonstrated practical application of AI in at least one project, prototype, workflow, or use case beyond code generation.
Optional to strengthen further:...and who can translate emerging AI capabilities into practical, scalable, and well-governed solutions.
Marsh (NYSE: MRSH) is a global leader in risk, reinsurance and capital, people and investments, and management consulting, advising clients in 130 countries. With annual revenue of over $27 billion and more than 95,000 colleagues, Marsh helps build the confidence to thrive through the power of perspective. For more information, visit marsh.com, or follow us on LinkedIn and X.
Marsh is committed to embracing a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, caste, disability, ethnic origin, family duties, gender orientation or expression, gender reassignment, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law.
Marsh is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one “anchor day” per week on which their full team will be together in person.
Required skills
python, generative ai, sql, javascript, java, c#, llm apis, prompt engineering, ai validation, ai security, agile, devops, nosql, code quality, ai prototyping, ai integration, ai deployment, ai monitoring, ai support, ai documentation, ai business alignment, ai compliance, ai process improvement, cross-functional collaboration, analytical thinking, critical thinking, collaboration, digital literacy
