Senior Data Scientist - NLP

We're Hiring: Senior Data Scientist NLP

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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 [HIDDEN TEXT]

Or apply directly on LinkedIn.