Location-

GurugramExperience-

2-3 years

Strong software engineering fundamentals in Python

— including writing clean, modular, testable code and designing maintainable codebases/architectures, not just scripting modelsProven 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 PyTorchDeep knowledge of LLMs and generative AI , including prompt engineering, retrieval-augmented generation (RAG), embeddings, and vector databasesExperience 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 servicesCloud platform experience

(Azure and/or AWS) and comfort deploying containerized workloads (Docker/Kubernetes) at scaleSolid 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 experimentationGit and standard version control practices





Strongly PreferredFamiliarity 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 dataUnderstanding of responsible AI, privacy, and security practices in AI systemsExperience with MCP (Model Context Protocol) tools or similar emerging agent-tooling standards

What You'll DoArchitect and build AI-powered applications and services designed to scale beyond a proof of conceptFine-tune and productionize ML/GenAI models, integrating them into existing products, workflows, and APIsDesign retrieval-augmented generation and agent-based workflows for real business use casesMake sound engineering trade-offs on system design, performance, and maintainability as usage grows

Soft SkillsStrong problem-solving and communication skills; able to explain technical trade-offs to non-technical stakeholdersComfortable owning ambiguous problems and working independently in a quick-paced environmentCollaborative — works well across engineering, data science, and product teams

📌 Data Science - Administrator (Mumbai)
🏢 Marsh
📍 Mumbai