Senior Data Scientist

Overview

Job Description

Key Responsibilities:

Leadership & Team Guidance

• Mentor and lead data scientists and ML engineers across multiple AI/ML and GenAI projects.
• Provide direction on solution design, model development, data pipelines, and deployment strategies.
• Set and enforce best practices in experimentation, reproducibility, code quality, and delivery excellence.

AI/ML & Data Science Expertise

• Design and implement end-to-end ML workflows across classical and modern data science:
• Regression, classification, clustering, time-series forecasting
• Anomaly detection, recommender systems
• Feature engineering, statistics, hypothesis testing, causal modelling
• Conduct research and build models across NLP, Computer Vision, and Reinforcement Learning.
• Use deep learning frameworks like PyTorch, TensorFlow, Hugging Face Transformers for model development.

Generative AI & Agentic AI Systems

• Build, fine-tune, and deploy Large Language Models (LLMs) using GPT, Llama, Mistral, Qwen, and similar.
• Implement and optimize Retrieval-Augmented Generation (RAG) pipelines.
• Build multi-agent workflows using frameworks like LangChain, LangGraph, CrewAI, or LlamaIndex.
• Rapidly prototype with Hugging Face Transformers and similar model libraries.

Production-Grade AI Delivery

• Architect and deploy large-scale, production-grade ML and GenAI systems with monitoring and reliability.
• Build and deploy ML APIs using FastAPI or Flask.
• Implement CI/CD pipelines, model monitoring, versioning, and ML/AgentOps (MLflow, LangFuse, evaluation frameworks).
• Optimize models for performance, latency, and cost efficiency across cloud and on-prem environments.

On-Prem GPU Infrastructure & Local LLM Hosting

• Manage and maintain on-premise GPU servers, including:
• CUDA & cuDNN installation and troubleshooting
• GPU driver setup & optimization
• NVIDIA toolkit, Docker GPU runtime configuration
• Set up, maintain, and administer JupyterHub/JupyterLab for team-wide model development.
• Host and run LLMs locally using:
• Ollama, llama.cpp, vLLM, text-generation-inference, or similar runtimes
• Optimize GPU memory usage, model quantization (gguf, int4/int8), and inference performance.
• Manage local deployment environments, containers, and dependencies for on-prem workflows.

Client Collaboration & Pre-Sales Engagement

• Engage in pre-sales discussions to understand client requirements and pain points.
• Recommend scalable, cost-efficient AI/ML/GenAI architectures (cloud or on-prem).
• Prepare and deliver technical presentations, solution proposals, and architecture diagrams.
• Translate complex AI concepts into clear narratives for business and technical stakeholders.

Innovation, Governance & Best Practices

• Stay up to date with advancements in AI, GenAI, ML engineering, and GPU acceleration.
• Build internal tools, playbooks, and frameworks to improve delivery and engineering practices.
• Drive compliance with enterprise standards around security, data governance, and reliability.

Required Qualifications

• 6+ years of hands-on experience in data science, machine learning, and production deployment.
• Strong team leadership, mentoring, and technical direction experience.
• Expertise in Python and ML/DL frameworks: PyTorch, TensorFlow, Hugging Face, Scikit-learn.
• Proven experience working with LLMs, transformers, RAG, and GenAI applications.
• Strong background in core data science (statistics, modelling, feature engineering, ML algorithms).
• Hands-on experience deploying production AI systems on cloud or on-prem environments.
• Experience maintaining on-prem GPU servers, CUDA installation, and driver management.
• Familiarity with JupyterHub/JupyterLab setup, user management, and environment provisioning.
• Experience hosting LLMs locally using Ollama, llama.cpp, vLLM, or similar.
• Strong communication skills, especially in pre-sales, technical architecture discussions, and client engagements.

Preferred Qualifications

• Experience with agentic AI frameworks such as LangChain, LangGraph, CrewAI, or LlamaIndex.
• Familiarity with MLflow, LangFuse, Prometheus, or other monitoring frameworks.
• Understanding of MLOps/DevOps practices, Docker/Kubernetes, and CI/CD pipelines.
• Contributions to open-source AI/ML or GenAI tooling.
• Certifications in cloud, AI/ML, or data engineering.