AIML Engineer

AIML Engineer - Intelligent Agentic Systems

FreeCharge is seeking a highly skilled AIML Engineer to conceptualize, develop, and deploy advanced intelligent agentic systems. This role focuses on solving complex, enterprise-scale, real-world challenges by leveraging cutting-edge AI frameworks, multimodal pipelines, MCP-based infrastructures, and agent-driven workflows. You will integrate autonomous reasoning with human-in-the-loop learning, making this position ideal for engineers passionate about hands-on model development, production deployments, and creating impactful AI capabilities.

Responsibilities:

• Design and implement intelligent, agent-driven systems for autonomous resolution of complex business problems using advanced AI algorithms.
• Develop collaborative multi-agent frameworks for coordinated reasoning and action in large-scale applications.
• Construct and enhance MCP-based infrastructure for secure, context-aware interactions with external tools and APIs.
• Create workflows that blend agent autonomy with human oversight, facilitating continuous learning via feedback loops (e.g., RLHF, in-context correction).
• Build, fine-tune, train, and evaluate machine learning and deep learning models using popular frameworks like PyTorch and TensorFlow.
• Process and utilize multimodal data pipelines (text, images, structured data) for embedding generation, feature extraction, and downstream tasks.
• Integrate AI models into production environments through APIs, inference pipelines, and monitoring tools.
• Utilize Git, testing frameworks, and CI/CD practices for high-quality, maintainable code.
• Document architectural decisions, trade-offs, and system behaviors to foster cross-team collaboration.
• Stay abreast of AI research advancements and apply them to product design in Delhi, Gurgaon, and Noida.

Requirements:

• Proficiency in Python and experience with Agentic frameworks.
• Strong grasp of machine learning fundamentals including optimization, representation learning, evaluation metrics, and supervised, unsupervised, and generative modeling.
• Practical experience with multimodal datasets and feature pipelines.
• Proven experience deploying ML models to production, encompassing inference optimization and monitoring.
• Familiarity with LLMOps/MLOps principles such as versioning, reproducibility, observability, and governance.