Lead Python AI / ML Engineer

FreeCharge is seeking a highly skilled Lead AI/ML Engineer specializing in Generative AI (GenAI) to spearhead the design, development, and deployment of sophisticated intelligent agentic systems. This pivotal role focuses on tackling complex, enterprise-scale challenges by leveraging advanced AI frameworks, multimodal pipelines, MCP-based infrastructures, and innovative agent-driven workflows. The position requires a deep integration of autonomous reasoning capabilities with effective human-in-the-loop learning mechanisms.

Key Responsibilities:

• Spearhead the design and deployment of intelligent agent-driven systems to address critical business problems.
• Engineer robust multi-agent frameworks to facilitate coordinated reasoning and actionable outcomes.
• Develop and enhance MCP-based infrastructure to ensure secure and efficient agent-tool/API interactions.
• Create advanced AI workflows that seamlessly integrate autonomous agents with essential human oversight.
• Implement sophisticated feedback loops, including Reinforcement Learning from Human Feedback (RLHF) and in-context correction strategies.
• Build, fine-tune, train, and rigorously evaluate Machine Learning and Deep Learning models using industry-standard tools such as PyTorch and TensorFlow.
• Process and work with diverse multimodal datasets, encompassing text, images, and structured data, for effective embedding generation and feature engineering.
• Deploy ML models into production environments via scalable APIs and optimized inference pipelines.
• Optimize inference performance and establish comprehensive monitoring systems to ensure operational excellence.
• Uphold high standards of code quality through rigorous use of Git, established testing frameworks, and robust CI/CD practices.
• Create clear and comprehensive documentation for architectural decisions and system designs.
• Continuously research and apply the latest advancements in AI to drive product innovation.

Essential Requirements:

• Exceptional proficiency in Python programming.
• A profound understanding of core ML fundamentals, including optimization techniques, representation learning, evaluation metrics, and various model types (supervised, unsupervised, generative).
• Proven hands-on experience with multimodal data pipelines.
• Demonstrated experience in deploying ML models into production environments.
• Solid knowledge of LLMOps/MLOps principles, covering versioning, observability, reproducibility, and governance.
• Experience working with agentic frameworks.
• Practical experience in designing goal-oriented agentic systems and managing multi-agent coordination.
• Hands-on expertise with frameworks such as LangChain, LangGraph, AutoGen, Google ADK, or similar technologies.
• Familiarity with MCP-based secure communication protocols.
• Experience in implementing Reinforcement Learning from Human Feedback (RLHF).
• Experience with major cloud platforms (AWS, GCP, Azure).