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Tier-1 brand and metro location but highly specialized senior role reduces applicant density.
Highly specialized RL and principal-level ML systems expertise reduces cross-industry transferability.
Explicit 12–17 years plus specialized RL and production ML requirements create strict shortlisting filters.
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Design and deploy advanced Reinforcement Learning (RL) models, including RLHF and multi-agent RL, for real-world decision-making applications.
Lead the architecture and delivery of scalable, production-grade AI/ML systems using MLOps best practices across enterprise platforms.
Provide technical leadership, mentorship, and architectural guidance to engineering teams while translating research into reliable AI solutions integrated with GenAI, LLMs, and agent-based frameworks.
12 to 17 years of experience in Machine Learning, AI engineering, or related domains.
Bachelor's or Master’s degree in Computer Science, AI, ML, or a related field.
Strong expertise and hands-on experience with Reinforcement Learning techniques and production AI/ML system deployment at scale.
Proficiency in Python programming, MLOps tools (e.g., MLflow, Kubeflow, SageMaker), and knowledge of Distributed Systems and Cloud platforms (AWS/Azure/GCP).
Senior-level AI/ML engineer with deep specialization in Reinforcement Learning and production system implementation.
Experience bridging cutting-edge research and scalable enterprise AI deployment, including agentic and multi-agent AI systems.
Capable technical leader who mentors teams, drives adoption of new AI techniques, and collaborates effectively across global interdisciplinary teams.