Meril Life
1 day ago
Description :Were Hiring : Senior Data Scientist - NLPCompany : NUVO AI (Meril Group)Location : Vapi, GujaratMode : On-SiteExperience : 5+ Years in Data Science/ML Engineering (Minimum 3 years tenure in most recent organization in a relevant data science/ML role)Senior-level data scientist role focused on building and deploying production NLP systems on bare metal infrastructure. This position requires a research-oriented mindset with the ability to build first-in-class products by translating cutting-edge research into innovative production solutions.If you enjoy solving deep technical problems and pushing innovation-this role is for you.What Youll Work On :- Build and deploy production-grade NLP systems using transformer models- Implement RAG pipelines, embeddings, semantic search- Manage and optimize bare-metal GPU servers, CUDA, and multi-GPU setups- Design and optimize large-scale SQL pipelines- Deploy and scale models via Drive MLOps: MLflow, W&B, monitoring, automated retraining- Lead ML architecture, research-to-production pipelines, and cross-functional collaborationRequired Skills :- 5+ years in ML/Data Science (with 3+ years in latest role)- Proven ML production deployment experience- Advanced SQL (tuning, indexing, Strong Python, PyTorch/TensorFlow, HuggingFace- Experience with LangChain/LlamaIndex, LoRA/QLoRA, vector DBs (Pinecone/FAISS)- Linux admin, GPU cluster management, distributed training- Docker, Kubernetes, CI/CD- Cloud: AWS/GCP/Azure + hybrid infrastructureWhat You Bring :- Research-driven mindset- Strong debugging, analytical & problem-solving skills- Ability to work independently and own complex projects- Clear communication with technical and non-technical teams- Experience moving models from research ? production- Up-to-date knowledge of modern ML and NLP advancementsProgramming & ML Frameworks :- Python (advanced level, production-grade code)- PyTorch or TensorFlow- HuggingFace Transformers- scikit-learn, XGBoost, LightGBMInfrastructure & DevOps :- Linux system administration- Bare metal server management- GPU cluster setup and configuration- CUDA/cuDNN installation and driver management- Multi-GPU distributed training setup- Docker and Kubernetes- CI/CD pipelines for ML workflowsProduction Deployment :- Model serving: TensorFlow Serving, TorchServe, FastAPI, BentoML- MLOps: MLflow, Weights & Biases, Kubeflow- Model monitoring and A/B testing- Latency optimization and inference scalingCloud & Data Engineering :- AWS, GCP, or Azure- Apache Spark, Airflow/Prefect- Understanding of on-premise and cloud hybrid architecturesLeadership & Team Impact :- Lead end-to-end ML initiatives- Define technical architecture- Mentor junior ML engineers and data scientists- Support hiring, code quality, and documentation standards (ref:hirist.tech)
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