We're Hiring: Senior Data Scientist NLP
Company: NUVO AI (Meril Group)
Location: Vapi, Gujarat
Mode: On-Site
Experience: 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 innovationthis role is for you.
What You'll 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 TorchServe/FastAPI/BentoML
- Drive MLOps: MLflow, W&B, monitoring, automated retraining
- Lead ML architecture, research-to-production pipelines, and cross-functional collaboration
Required Skills
- 5+ years in ML/Data Science (with 3+ years in latest role)
- Proven ML production deployment experience
- Advanced SQL (tuning, indexing, Snowflake/BigQuery/Redshift)
- 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 infrastructure
What 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 advancements
Programming & ML Frameworks
- Python (advanced level, production-grade code)
- PyTorch or TensorFlow
- HuggingFace Transformers
- scikit-learn, XGBoost, LightGBM
Infrastructure & 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 workflows
Production Deployment
- Model serving: TensorFlow Serving, TorchServe, FastAPI, BentoML
- MLOps: MLflow, Weights & Biases, Kubeflow
- Model monitoring and A/B testing
- Latency optimization and inference scaling
Cloud & Data Engineering
- AWS, GCP, or Azure
- Apache Spark, Airflow/Prefect
- Understanding of on-premise and cloud hybrid architectures
Leadership & 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
How to Apply
Send your resume to [Confidential Information]
Or apply directly on LinkedIn.
