Meril Life - Senior Data Scientist - NLP

Description :

Were 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 innovation-this role is for you.

What Youll Work On :

- 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 :

- 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 :

- 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 :

- PyTorch or TensorFlow

- HuggingFace Transformers

- Scikit-learn, XGBoost, LightGBM

Infrastructure & DevOps :

- 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 :

- MLOps : MLflow, Weights & Biases, Kubeflow

- Model monitoring and A/B testing

- Latency optimization and inference scaling

Cloud & Data Engineering :

- Apache Spark, Airflow/Prefect

- Understanding of on-premise and cloud hybrid architectures

Leadership & Team Impact :

- Define technical architecture

- Mentor junior ML engineers and data scientists

- Support hiring, code quality, and documentation standards