Lead AI Engineer

Role Overview We are seeking a Lead Generative AI Engineer with strong foundations in deep learning, transformer architecture, and practical experience building GenAI applications beyond basic RAG systems. The ideal candidate has hands-on experience/technical familiarity with LLM fine-tuning, multimodal models, retrieval systems, agentic frameworks, retrieval architectures, and production-grade ML deployment. This role will partner with engineering, data science, and CX teams to build intelligent agents, multimodal experiences, personalization systems, and knowledge-grounded AI solutions that power the future of customer engagement for global brands. Experience Requirements Minimum 3-6 years of hands-on software development experience including building and deploying machine learning models into production. 2+ years of experience working with deep learning, GenAI , or transformer-based architectures. Demonstrated experience building GenAI applications beyond simple RAG (e.g., agents, multimodal, custom LLM fine-tuning). Experience integrating AI systems in enterprise-grade environments. Location: Pune,Mumbai,Bengaluru,Gurugram,Chennai,Coimbatore Work mode : Hybrid (3-4 days a week from office) Required Technical Skills Programming: Python (advanced), SQL; robust experience with API development and data engineering, Backend Frameworks: Flask, FASTAPI, Django Machine Learning: Predictive modelling, deep learning, optimization, embeddings, vector search, model evaluation. Generative AI: LLMs, RAG, multimodal architectures, agents, prompt engineering, grounding, knowledge graphs. Cloud Platforms: AWS, Azure, or GCP with hands-on experience deploying and scaling AI systems. Data Technologies: Apache Spark, Hadoop, MongoDB; strong understanding of data pipelines and large-scale processing. Math Foundations: Linear algebra, probability, statistics. Key Responsibilities Generative AI, Multimodal Systems & Agentic Frameworks Build conversational and non-conversational, multimodal, and agentic AI applications using LLMs and frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or similar. Deployment, APIs & Cloud Engineering Transform models into scalable APIs and microservices using Python, FastAPI/Flask, Docker. Model Development & Applied AI Engineering Build and optimize transformer-based and multimodal models using deep learning frameworks (e.g., PyTorch, TensorFlow). Collaboration, Documentation & Mentorship Work cross-functionally with CX, engineering, and product stakeholders to translate business needs into AI solutions.