AIML

FreeCharge is seeking a skilled SSDE Python ML professional to architect, develop, and deploy advanced intelligent agentic systems designed to tackle complex, real-world challenges at an enterprise scale. This role involves working with state-of-the-art AI frameworks, multimodal data pipelines, MCP-based infrastructures, and sophisticated agent-driven workflows that blend autonomous reasoning with human-in-the-loop learning. It is an excellent opportunity for hands-on engineers passionate about building robust, production-grade AI systems that deliver tangible business results.

Key Responsibilities:

• Design and implement intelligent, agent-driven systems capable of autonomously resolving intricate business problems.
• Develop collaborative multi-agent frameworks that facilitate coordinated reasoning and action.
• Construct and enhance MCP-based infrastructure to ensure secure and context-aware interactions between agents and tools.
• Create workflows that balance agent autonomy with essential human oversight.
• Implement continuous learning mechanisms through feedback loops, such as Reinforcement Learning from Human Feedback (RLHF) and in-context correction.
• Build, fine-tune, train, and evaluate Machine Learning and Deep Learning models utilizing PyTorch and TensorFlow.
• Process and manage multimodal data, including text, images, and structured data, through robust pipelines.
• Integrate AI models into production environments via APIs, efficient inference pipelines, and comprehensive monitoring systems.
• Adhere to industry best practices by employing Git, established testing frameworks, and Continuous Integration/Continuous Deployment (CI/CD) pipelines.
• Document system architecture, critical design decisions, and underlying trade-offs.
• Maintain an up-to-date understanding of AI research and apply emerging advancements to enhance product development.

Essential Requirements:

• Advanced proficiency in Python and experience with agentic frameworks.
• Strong foundational knowledge of Machine Learning principles, including optimization, representation learning, and evaluation metrics.
• Demonstrated experience with supervised, unsupervised, and generative modeling techniques.
• Practical experience working with multimodal datasets and feature engineering pipelines.
• Proven track record of deploying ML models into production environments, including inference optimization and performance monitoring.
• Familiarity with LLMOps/MLOps concepts, such as version control, reproducibility, observability, and governance.

Beneficial Skills:

• Experience in designing goal-oriented agentic systems and multi-agent coordination workflows.
• Exposure to frameworks like LangChain, LangGraph, AutoGen, or Google ADK.
• Knowledge of secure agent/tool communication protocols, particularly MCP.
• Experience with RLHF and reward modeling.
• Familiarity with cloud platforms.