Lead AI Engineer

Role Overview

We are seeking a LeadGenerative 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.