DM-JD: Data Science & AI Engineer

We are looking for a candidate with a robust foundation in Data Science: predictive and forecasting who has built few Generative AI systems, with at least one production-grade GenAI deployment under their belt. The ideal candidate has spent the couple of years building forecasting models, time-series pipelines, and statistical/ML systems, and has since shipped and operated a real GenAI System in production — not just a POC or hackathon build. You'll bring quantitative depth to areas like demand/price forecasting while owning GenAI architecture, observability, and agentic workflows.

Roles & Responsibilities

Design and develop scalable GenAI applications, copilots, and chatbot systems
Build and optimize Retrieval Augmented Generation (RAG) pipelines
Develop agentic workflows using LangGraph/LangChain
Apply forecasting and predictive modeling expertise to renewable energy use cases (e.g., generation forecasting, price/demand forecasting, asset performance prediction)
Design and build APIs and AI microservices using FastAPI or similar frameworks




Develop observability and monitoring pipelines using OpenTelemetry, LangSmith, Grafana, or similar tools
Optimize AI systems for latency, scalability, reliability, and cost
Collaborate with cross-functional teams to deploy production-grade AI and DS solutions

Technical Skills

Must Have:

At least 1-2 production-grade GenAI projects shipped and operated live — specifically a RAG-based chatbot, copilot, or assistant serving real users/traffic (not a prototype). Should be able to speak to real production concerns: latency, cost, scale, failure modes, monitoring, and iteration post-launch
Strong hands-on experience in predictive/forecasting data science — time-series modeling, regression, ensemble methods (e.g., LightGBM, XGBoost), or deep learning forecasting architectures
Solid grounding in statistical modeling, feature engineering, and model evaluation for forecasting problems
Hands-on exper

📌 Data Scientist (Gurugram)
🏢 RENEW
📍 Gurugram