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Tier-1 employer and metro location increase applicant density despite senior, specialized role.
Highly domain-specific ML/RL and production experience reduces cross-industry portability.
Explicit 12–17 years, deep RL/production ML requirements and leadership expectations make screening highly selective.
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Design and develop Reinforcement Learning models including RLHF and multi-agent RL for real-world decision tasks.
Build and deploy scalable production AI/ML systems using MLOps best practices and cloud-based distributed systems.
Provide technical leadership and mentorship while translating research AI concepts into reliable enterprise-grade solutions.
12 to 17 years of experience in ML/AI engineering or related domains.
Strong expertise in Reinforcement Learning techniques such as Deep RL, Policy Optimization, and RLHF.
Proficient in Python programming and experienced with MLOps tools (MLflow, Kubeflow, SageMaker).
Bachelor’s or Master’s degree in Computer Science, AI, ML, or related field.
Experienced in end-to-end AI system architecture integrating RL with GenAI, LLMs, or agent-based frameworks.
Demonstrated ability to lead technical teams and mentor engineers in a global enterprise environment.
Skilled at translating complex AI research into operational, scalable business solutions.