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Lead Data & AI Scientist

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Job Description - Lead Data & AI Scientist

End Date

Sunday 14 June 2026

We Support Flexible Working – Click here for more information on flexible working options

Flexible Working Options

Hybrid Working

Job Description Summary

Leads the desigining of patterns, tooling, and templates to rapidly and efficiently deploy models into production. Scales AI/ML solutions on strategic infrastructure and integrating them into existing systems. Acts as a point of technical expertise and thought leadership, and provides line management and/or coaching to grow team capability.

Job Description

Exp : 15+ years
Location : Hyderabad

 

We are looking for a hands-on Lead Data & AI Scientist to drive the design, development, and scaling of AI-powered solutions across the enterprise. This role will shape our AI/ML strategy, mentor high-performing teams, and deliver impactful outcomes using cutting-edge technologies like GenAI, autonomous agents, NLP, and advanced ML.

🔹 What You’ll Do

  • Lead end-to-end development of scalable AI/ML solutions for complex business problems
  • Design and implement GenAI systems (RAG pipelines, autonomous agents)
  • Evaluate and optimise LLMs (GPT, LLaMA, Claude, Mistral) for business use cases
  • Collaborate with cross-functional teams to integrate AI into products/platforms
  • Drive experimentation, rapid prototyping, and continuous innovation
  • Mentor data scientists and engineers, fostering a culture of excellence
  • Establish best practices for MLOps, governance, and model lifecycle management

🔹 What We’re Looking For

  • Strong foundation in Statistics, Machine Learning, and Deep Learning
  • Hands-on experience with GenAI frameworks (LangChain, LangGraph, etc.)
  • Expertise in NLP (semantic search, NER, text generation)
  • Experience building and deploying LLM-based solutions and fine-tuning models
  • Ability to design autonomous AI agents (planning, reasoning, action)
  • Proven experience working with large datasets and scalable AI systems
  • Exposure to MLOps/AIOps for production-grade deployments
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