Overview

As a Lead Gen-AI & AI/ML Engineer, you will lead the design and engineering of advanced agentic AI systems, drive architecture decisions, and guide a team toward delivering high-impact AI solutions. You will be responsible for scaling AI/ML capabilities across the organization, owning production-grade deployments, and ensuring that our agentic platforms are robust, secure, and aligned with business goals. This is a leadership role requiring deep technical expertise as well as team management experience.

Responsibilities

• About the Role
• As a Lead Gen-AI & AI/ML Engineer, you will lead the design and engineering of advanced
• agentic AI systems, drive architecture decisions, and guide a team toward delivering
• high-impact AI solutions. You will be responsible for scaling AI/ML capabilities across the
• organization, owning production-grade deployments, and ensuring that our agentic platforms
• are robust, secure, and aligned with business goals. This is a leadership role requiring deep
• technical expertise as well as team management experience.
• Strategic Ownership & Architecture
• ● Own the architecture of enterprise-grade agentic AI systems and multi-agent workflows.
• ● Define engineering best practices and guide technical decisions across Gen-AI, ML, and
• automation initiatives.
• ● Evaluate emerging AI research and technologies for integration into platforms and
• ● Lead cross-functional discussions with engineering, product, business, and data teams.
• Agentic Systems & MCP Infrastructure
• ● Architect goal-driven agent frameworks capable of reasoning, planning, and taking
• autonomous actions.
• ● Build and evolve Model Context Protocol (MCP) infrastructure for secure, context-rich
• interactions with tools, APIs, databases, and cloud systems.
• ● Oversee design of long-running, stateful agent pipelines with strong error recovery,
• coordination strategies, and dynamic task management.
• Advanced AI/ML Engineering
• ● Lead the development of ML/DL models, including generative models, multimodal
• pipelines, and retrieval-augmented architectures.
• ● Ensure scalable, observable, and reliable ML operations (MLOps/LLMOps) across the
• ● Guide teams in model evaluation, benchmarking, optimization, and production
• deployment.
• Human-in-the-Loop & Continuous Learning Systems
• ● Design and supervise RLHF processes, feedback loops, and reward modeling
• frameworks.
• ● Ensure systems evolve through continuous human feedback and real-world interactions.