Principal Machine Learning Engineer

Role Summary

Principal-level AIML Engineer responsible for designing and deploying advanced AI/ML systems with a strong focus on Reinforcement Learning (RL) and decision intelligence. Will act as a technical leader driving scalable AI solutions from research to production across enterprise platforms.

Key Responsibilities

Design and develop Reinforcement Learning models (RL, RLHF, multi-agent RL) for real-world decision-making problems

Build and deploy scalable ML pipelines and production AI systems using MLOps best practices

Architect end-to-end AI systems integrating RL with GenAI, LLMs, or agent-based frameworks

Lead development of agent-based / multi-agent AI systems for planning, reasoning, and automation

Translate research concepts into production-grade, reliable ML systems

Partner with data scientists, engineers, and product teams to deliver enterprise AI solutions

Evaluate new AI techniques (RLHF, agentic systems, deep RL) and drive adoption

Mentor engineers and provide technical leadership and architectural guidance

Must-Have Skills

Strong expertise in Reinforcement Learning (Deep RL, Policy Optimization, RLHF)

Hands-on experience building production AI/ML systems at scale

Strong programming in Python

Experience with MLOps (MLflow, Kubeflow, SageMaker, etc.)

Knowledge of Distributed Systems & Cloud (AWS/Azure/GCP)

Experience in model deployment, monitoring, and lifecycle management

Strong understanding of ML/DL algorithms and optimization techniques

Good-to-Have

Experience with multi-agent systems / agentic AI frameworks

Exposure to LLMs, RAG, or Generative AI systems

Experience with simulation environments (Gym, RLlib, etc.)

Background in optimization, control systems, or operations research