DM-JD: Data Science & AI Engineer We are looking for a candidate with a strong 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 experience with LangGraph, LangChain, or similar orchestration frameworks

Strong understanding of RAG architecture — embeddings, chunking strategies, retrieval tuning, and vector search

Strong Python programming skills

Experience building REST APIs using FastAPI

Hands-on experience with vector databases/search platforms such as Azure AI Search, Pinecone, Milvus, or FAISS

Experience with observability tools like OpenTelemetry, LangSmith, Langfuse, Grafana, or Azure Monitor

Familiarity with cloud platforms such as Azure, AWS, or GCP Positive to Have: Prior experience in energy/utilities/manufacturing domains involving forecasting (demand, price, generation, or maintenance)

Experience with semantic caching, guardrails, or query rewriting in production RAG systems

Experience with multimodal AI systems

Exposure to Docker, Kubernetes, and CI/CD pipelines

Knowledge of AI safety, guardrails, and prompt engineering Eligibility Criteria Strong system design and problem-solving skills

Ability to bridge classical ML/forecasting rigor with modern GenAI system design

Excellent communication and collaboration abilities

Should be able to walk through architecture and post-launch learnings of a shipped RAG/chatbot system in an interview

📌 Data Scientist (Gurugram)
🏢 RENEW
📍 Gurugram