Data Science - Administrator

Location- Gurugram

Experience- 2-3 years

• 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