Lead design, development, and scaling of AI/ML solutions (GenAI, LLMs, autonomous agents). Build production-grade LLM systems, RAG pipelines, and MLOps practices. Mentor teams, drive experimentation, integrate AI into products, and establish governance and model lifecycle best practices.
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Hybrid WorkingJob 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
- 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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