Lead Generative AI Engineer role requiring strong expertise in deep learning, transformer architectures, and building GenAI applications beyond basic RAG systems. Responsibilities include developing multimodal and agentic AI applications, designing AI workflows with reasoning and external integrations, building Knowledge Graph-assisted AI systems, ensuring model safety and consistency, transforming models into scalable APIs and microservices, deploying and monitoring ML/AI systems on cloud platforms (AWS, Azure, GCP), collaborating on CI/CD pipelines and MLOps, working with big data technologies (Apache Spark, Hadoop, MongoDB), building and optimizing transformer-based models, implementing fine-tuning and alignment techniques, developing information retrieval systems, building predictive ML pipelines, cross-functional collaboration, documentation, and mentoring junior engineers. Requires 5-6 years software development experience including ML production deployment, 2+ years deep learning/GenAI experience, advanced Python and backend framework skills, cloud deployment experience, strong math foundations, and ability to communicate complex concepts. Hybrid work model with minimum 2 days in office in Pune, Maharashtra, India.
What you'll do
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Build conversational and non-conversational, multimodal, and agentic AI applications using LLMs and frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or similar
Design AI workflows incorporating reasoning, planning, tool-use, memory, grounding, and external system integrations
Develop Knowledge Graph-assisted AI systems including entity extraction, linking, and KG-augmented retrieval
Ensure safety, consistency, and hallucination-control through structured evaluation and guardrails
Transform models into scalable APIs and microservices using Python, FastAPI/Flask, Docker
Deploy and monitor ML/AI systems in AWS/Azure/GCP, optimizing for cost, latency, and reliability
Collaborate with MLOps teams on CI/CD pipelines, model versioning, monitoring, and automated evaluation
Work with big data technologies including Apache Spark, Hadoop, and MongoDB
Build and optimize transformer-based and multimodal models using deep learning frameworks
Implement fine-tuning, alignment (RLHF/RLAIF), LoRA/QLoRA, pruning, and model evaluation pipelines
Develop information retrieval systems including hybrid dense–sparse retrieval, ranking, knowledge graphs, and relevance optimization
Build predictive models and ML pipelines from scratch including data preparation, feature engineering, and model selection
Work cross-functionally with CX, engineering, and product stakeholders to translate business needs into AI solutions
Document models, experiments, evaluation frameworks, and deployment processes
Mentor junior engineers and contribute to internal best practices, reusable components, and R&D initiatives
Requirements
Minimum 5-6 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
