Lead AI/ML Product

As an Expert AI/ML Product Lead, you play a crucial role in driving the end-to-end development of the Autonomous ML Engineer product. Your responsibilities include leading the development of AI products, focusing on Generative AI and agent-based systems. Additionally, you will be responsible for enterprise-grade product development to ensure the AI/ML platform meets strict enterprise requirements for security, compliance, scalability, and reliability. Key Responsibilities: - Lead the development of AI products with a specific focus on Generative AI and agent-based systems. - Ensure enterprise-grade product development to meet stringent enterprise needs for security, compliance, scalability, and reliability. - Architect and implement ML and data engineering workflows, integrating pipelines for Datalakes and large-scale compute clusters. - Perform hands-on development using Python for model development and integration. - Design and execute model fine-tuning, training, and evaluation processes. - Build and optimize AI agent implementation workflows. - Establish MLOps practices for reliable scaling and monitoring of models. - Manage GPU compute clusters and Kubernetes deployments for high-performance AI systems. - Collaborate with cross-functional teams to align technical capabilities with business requirements. - Oversee product releases, coordinate feature rollouts, and manage SLAs to ensure timely delivery and minimal downtime. - Maintain thorough documentation, including technical specs and user guides. - Engage directly with key enterprise clients to gather insights, feedback, emerging requirements, drive adoption, and address high-priority issues. Required Qualifications: - 5+ years of hands-on experience in AI/ML product development. - Expert-level Python programming skills. - Proven experience with data engineering on large-scale systems (Spark, HPC) and data ingestion pipelines. - Deep knowledge of Generative AI model training and fine-tuning. - Experience building and deploying AI agents in production/dev environments. - Strong background in MLOps, including monitoring and scaling models. - Experience with GPU compute clusters and Kubernetes orchestration. - Excellent communication skills and ability to translate technical concepts. - Lead the entire product development with cross-functional teams with close coordination and sync-ups. - Familiarity with frameworks and tools (Docker, Kubernetes, Spark, TensorFlow, PyTorch) and cloud platforms (AWS, GCP, Azure). - Strong knowledge of AI/ML workflows, MLOps (CI/CD, model deployment, model registry), and data engineering principles.,