Lead Generative AI Engineer role requiring strong expertise in deep learning, transformer architectures, and building advanced 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, deploying scalable APIs and microservices using Python frameworks and Docker, monitoring ML systems on AWS/Azure/GCP, collaborating on CI/CD and MLOps pipelines, working with big data technologies like Apache Spark, Hadoop, and MongoDB, optimizing transformer-based models, implementing fine-tuning and alignment techniques, developing information retrieval and predictive modeling systems, cross-functional collaboration, documentation, and mentoring junior engineers. Requirements include 5-6 years software development experience with ML production deployment, 2+ years deep learning and GenAI experience, advanced Python and backend framework skills, cloud platform expertise, strong data engineering knowledge, and mathematical foundations. The role is hybrid 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
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 NoSQL databases such as 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
