DM-JD: Data Science & AI Engineer

We are looking for a candidate with a solid 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

Good 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