Lead AI / ML Engineer

We are actively seeking a skilled Lead Generative AI & AI/ML Engineer to join our dynamic team in Gurgaon. This role focuses on the end-to-end lifecycle of intelligent agentic systems, from conceptualization and design through to building and deploying at enterprise scale. You will be at the forefront of innovation, working with state-of-the-art AI frameworks, multimodal processing pipelines, MCP-based infrastructures, and advanced agent-driven workflows. This position is perfect for engineers who thrive on hands-on model development, creating production-ready deployments, and architecting sophisticated AI capabilities that deliver tangible business impact.

Key Responsibilities:

• Architect and implement intelligent, agent-driven systems capable of autonomously resolving complex business challenges using advanced AI algorithms.
• Develop and engineer collaborative multi-agent frameworks designed for coordinated reasoning and synchronized actions in large-scale applications.
• Construct and enhance MCP-based infrastructure to facilitate secure, context-aware interactions between AI agents and external tools or APIs.
• Design and build workflows that integrate agent autonomy with essential human oversight, fostering continuous improvement through feedback mechanisms like RLHF and in-context correction.
• Lead the building, fine-tuning, training, and rigorous evaluation of machine learning and deep learning models, leveraging frameworks such as PyTorch and TensorFlow.
• Manage and optimize multimodal data pipelines encompassing text, images, and structured data for embedding generation, feature extraction, and various downstream AI tasks.
• Seamlessly integrate developed models into production environments via robust APIs, efficient inference pipelines, and comprehensive monitoring tools.
• Adhere to best practices in software development, including Git for version control, comprehensive testing frameworks, and CI/CD pipelines to ensure high-quality, maintainable code.
• Produce clear and thorough documentation for architectural decisions, system trade-offs, and operational behavior to facilitate cross-team collaboration.
• Maintain an up-to-date understanding of emerging AI research trends and proactively apply relevant advancements to enhance product design and capabilities.

Essential Requirements:

• Proficiency in Python and experience with various agentic frameworks.
• A strong foundational understanding of machine learning principles, including optimization techniques, representation learning, evaluation metrics, and supervised, unsupervised, and generative modeling approaches.
• Practical experience working with multimodal datasets and building efficient feature pipelines.
• Proven experience in deploying machine learning models to production, covering aspects of inference optimization and performance monitoring.
• Familiarity with LLMOps/MLOps principles, encompassing model versioning, ensuring reproducibility, establishing observability, and implementing governance strategies.