Research, design, development, and deployment of advanced AI agents and agentic systems. Architect and implement complex multi-agent systems, including planning, decision-making, and execution capabilities. Develop and integrate large language models (LLMs) and other state-of-the-art AI techniques to enhance agent autonomy and intelligence. Build robust, scalable, and reliable infrastructure to support the deployment and operation of AI agents at scale. Collaborate with product managers, UX designers, and other engineers to define requirements and deliver impactful solutions. Diagnose and troubleshoot issues in complex distributed environments and optimize system performance. Contribute to the team's technical growth and knowledge sharing. Stay up-to-date with the latest advancements in AI research and agentic AI and apply them to our products. Leverage enterprise data, market data, and user interactions to build intelligent and personalized agent experiences. Contribute to the development of Copilot GenAI Workflows for Users, enabling chat-like command execution. Knowledge and passion in machine learning algorithms, Gen AI, LLMs, and natural language processing (NLP). Understanding of agent-based modeling, reinforcement learning, and autonomous systems. Experience with large language models (LLMs) and their applications in Agentic AI. Proficiency in programming languages such as Python, and experience with machine learning frameworks like TensorFlow or PyTorch. Experience with cloud platforms (AWS) and containerization technologies (Docker, Kubernetes). Understanding of distributed system design patterns and microservices architecture. Experience with message queuing systems (AWS SQS, Kafka). Hands-on experience with system integration patterns and API design. Excellent problem-solving and data analysis skills. Strong communication and collaboration skills. Master's or Ph.D. in Computer Science, Artificial Intelligence, or a related field, or equivalent years of experience. Min 5-7+ years of relevant work experience in AI, Machine Learning, and applying data science to real-world use cases. Strong track record of taking systems from prototype to production with a focus on scalability and reliability. Experience with RAG architectures, including hybrid retrieval, vector databases (Pinecone, pgvector), and rerankers. Proficiency in building multi-agent workflows using frameworks like LangGraph, CrewAI, or AutoGen. Knowledge of fine-tuning strategies (QLORA, DPO) and inference optimization (vLLM, TensorRT-LLM). Research experience in agentic AI or related fields. Experience building and deploying AI agents in real-world applications.