Data Science Lead

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About the Role

AI / Generative AI Technical Lead

This is a hands-on technical leadership role, involving solution design, implementation, and mentoring of development teams to deliver enterprise-grade AI/GenAI projects.

Key Responsibilities

• Design and implement

  end-to-end ML and GenAI solutions

  — including data collection, model training, deployment, and monitoring.
• Build

  production-grade ML pipelines

  (data preprocessing, feature engineering, model building, validation, and performance tuning).
• Design and develop

  Generative AI applications

  using open-source LLMs and cloud-based platforms.
• Work with

  Computer Vision and Core ML

  frameworks to deliver real-world AI use cases.
• Implement

  RAG architectures

  , fine-tune models (OpenAI, Llama, Mistral, Hugging Face, etc.), and optimize performance.
• Build

  API-based integrations

  for AI and Data Science applications.
• Collaborate with pre-sales and solution teams to design POCs and technical proposals.
• Mentor junior data scientists and engineers.
• Ensure best practices in version control, DevOps, and model governance.

Required Skills & Qualifications

• 6–10 years

  of total experience, with strong expertise in

  Data Science, AI, and Generative AI

  .
• Proven record of:

• 2–3 ML applications

  deployed to production.

• 1–2 Generative AI applications

  deployed to production.

• 3+ AI/Data Science projects

  delivered end-to-end.
• Hands-on coding experience with

  Python, PyTorch, TensorFlow, Spark/PySpark, SQL, NLP frameworks

  .
• Strong understanding of

  GenAI tools

  ,

  RAG

  , and

  LLM fine-tuning

  .
• Experience with

  OpenAI, Llama, Mistral, Hugging Face models

  .
• Knowledge of

  data engineering workflows

  – data lakes, pipelines, and warehouses.
• Familiarity with

  Docker, Kubernetes

  , and

  DevOps deployment pipelines

  .
• Excellent problem-solving, analytical, and communication skills.

Nice-to-Have (Preferred Skills)

• Hands-on experience with

  Azure AI, Databricks

  , or

  Google Cloud Vertex AI

  .
• Understanding of

  SaaS/enterprise-grade AI architectures

  .
• Exposure to

  configuration management

  and

  CI/CD automation tools

  .