Role Overview
We are looking for a skilled ML / Generative AI Engineer with strong hands-on experience in building and deploying production-grade Generative AI and Agentic AI systems. The role involves designing scalable, multi-agent AI solutions to solve real-world business problems using modern LLM frameworks and cloud infrastructure.
Key Responsibilities
- Design, develop, and deploy Generative AI solutions for text, image, and content-based applications.
- Build Agentic AI and multi-agent workflows using LangGraph, LangChain, and custom orchestration logic.
- Work with open-source LLMs (LLaMA, Mistral, Falcon, BERT, etc.) and implement RAG architectures.
- Preprocess and analyze large datasets for training and fine-tuning generative models.
- Optimize models for performance, latency, scalability, and resource efficiency.
- Deploy and manage AI solutions on AWS / Azure, following production best practices.
- Define and implement evaluation metrics to assess the quality and reliability of generative models.
- Design scalable architectures and build production-ready AI systems.
- Contribute to the company’s intellectual property by researching and developing novel AI approaches to address business challenges.
- Work closely with cross-functional teams (data science, engineering, product) to integrate AI into real-world applications.
- Stay updated with the latest research and advancements in Generative AI and LLMs.
- B.Tech / M.Tech in Computer Science, IT, or a related field.
- 3–6 years of hands-on experience in Machine Learning or AI.
- Practical experience with Generative AI, LLMs, Agentic AI, multi-agent systems, RAG, and vector databases.
- Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Solid understanding of neural networks, optimization techniques, and training methodologies.
- Hands-on experience deploying AI systems on AWS or Azure (mandatory).