Data Science - Administrator

Marsh; Gurugram District; Hybrid; INR 3,500,000 - 6,500,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Marsh is seeking an experienced ML/GenAI engineer to design and build AI-powered applications and services capable of scaling beyond prototypes. You will productionize models and integrate them into existing products, workflows, and APIs.

You will design retrieval-augmented generation and agent-based workflows, balancing system design with performance and maintainability as usage expands. Collaboration with engineering and data teams is essential.

Qualifications

• Strong Python fundamentals: clean, modular, testable code and scalable architectures.
• Experience end-to-end ML/GenAI: design to production deployment with ML frameworks.
• Deep knowledge of LLMs, prompt engineering, and RAG technologies.
• Experience designing AI agents and automation workflows beyond API calls.
• Backend/API development to integrate AI models into products/services.
• Cloud experience (Azure and/or AWS) with containerized deployments (Docker/Kubernetes).
• Solid ML lifecycle knowledge: data preprocessing, evaluation, monitoring, deployment.
• Git and standard version control practices.

Responsibilities

• Architect and build AI-powered applications designed to scale beyond PoCs.
• Fine-tune and productionize ML/GenAI models integrated into products and APIs.
• Design retrieval-augmented generation and agent-based workflows for real use cases.
• Make engineering trade-offs on system design, performance, and maintainability as usage grows.

Skills

Python

ML/GenAI

LLMs

Prompt design

RAG

Embeddings

Vector databases

Agent workflows

API development

Cloud (Azure/AWS)

Docker/Kubernetes

ML lifecycle

Git

Tools

Scikit-learn

TensorFlow

PyTorch

Docker

Kubernetes

Job description

Job Description:

• Strong software engineering fundamentals in Python — including writing clean, modular, testable code and designing maintainable codebases/architectures, not just scripting models
• Proven experience building and deploying ML/GenAI solutions end-to-end — from model/prompt design through to production deployment, using frameworks such as scikit-learn, TensorFlow, or PyTorch
• Deep knowledge of LLMs and generative AI, including prompt engineering, retrieval-augmented generation (RAG), embeddings, and vector databases
• Experience designing and building AI agents and automation workflows (not just calling APIs — architecting multi-step, tool-using systems)
• Backend and API development experience, with the ability to integrate AI models cleanly into existing products and services
• Cloud platform experience (Azure and/or AWS) and comfort deploying containerized workloads (Docker/Kubernetes) at scale
• Solid grasp of the full ML lifecycle: data preprocessing, model evaluation, monitoring, and deployment — with an eye toward reliability and scalability in production, not just notebook experimentation
• Git and standard version control practices

Strongly Preferred

• Familiarity with MLOps tooling and practices (CI/CD for ML, model versioning, monitoring/observability)
• Experience with data pipelines and ETL processes, and working with both structured and unstructured data
• Understanding of responsible AI, privacy, and security practices in AI systems
• Experience with MCP (Model Context Protocol) tools or similar emerging agent-tooling standards

What You'll Do

• Architect and build AI-powered applications and services designed to scale beyond a proof of concept
• Fine-tune and productionize ML/GenAI models, integrating them into existing products, workflows, and APIs
• Design retrieval-augmented generation and agent-based workflows for real business use cases
• Make sound engineering trade-offs on system design, performance, and maintainability as usage grows

Soft Skills

• Strong problem-solving and communication skills; able to explain technical trade-offs to non-technical stakeholders
• Comfortable owning ambiguous problems and working independently in a fast-paced environment
• Collaborative — works well across engineering, data science, and product teams

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.

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Requirements: