Senior Data Scientist
Location: Noida ,Uttar Pradesh, India
Experience: 5+ Years
Job Type: Full-time
Industry: Energy / Engineering (AI & Automation Hub)
About the Job
The Senior Data Scientist will design, build, and deploy advanced AI/ML and Generative AI solutions to support digital transformation across Technip Energies. This role involves end-to-end development—from research and modeling to scalable deployment using modern AI/ML frameworks.
Key Responsibilities
- Develop and deploy ML/DL and Generative AI models including RAG, LLM fine-tuning, and multi-agent workflows.
- Build and optimize deep learning pipelines using PyTorch, TensorFlow, JAX, and CUDA-based acceleration.
- Work with LLM frameworks (LangChain, LangGraph, LlamaIndex) for agentic automation and tool integrations.
- Implement MLOps workflows using MLflow, W&B, Kubeflow; automate model training, deployment, and monitoring.
- Optimize models using quantization, pruning, ONNX, TensorRT, and cloud-scale GPU acceleration.
- Deploy models as scalable microservices using Docker, Kubernetes, REST/gRPC APIs, and Azure ML.
- Collaborate cross-functionally to convert business problems into AI use cases and deliver production-ready solutions.
- Monitor model performance and drift using Azure Monitor, Prometheus, Grafana, and ELK stack.
Required Qualifications
- Master’s or Bachelor’s degree in Computer Science, Data Science, or related field.
- 5+ years of hands-on experience in ML/AI with strong understanding of ML theory and algorithms.
- Proven experience delivering end-to-end Generative AI solutions into production.
- Strong expertise in Python, ML/DL frameworks, and cloud-based AI deployments.
- Experience in building scalable AI systems using microservices, GPUs, and distributed environments.
- Strong analytical, debugging, and problem-solving skills.
Preferred Skills
- Experience with fine-tuning LLMs (LoRA/QLoRA), diffusion models, and advanced prompt engineering.
- Knowledge of graph-based RAG, vector databases (FAISS/Pinecone/Weaviate/Milvus).
- Hands-on experience with GNNs, time-series forecasting models, and reinforcement learning.
- Familiarity with Azure AI/ML services and modern observability frameworks.
- Publications, open-source contributions, or patents showcasing applied research.