Meril - Senior Data Scientist - NLP

Description :

- 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 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 to 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