Agentic AI Engineer - RL
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ABOUT XENONSTACK<\/b>
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XenonStack is the fastest\-growing <\/span>Data and AI Foundry for Agentic Systems<\/b>, enabling people and organizations to gain <\/span>real\-time and intelligent business insights<\/b>. We deliver innovation through: Akira AI<\/span><\/a><\/b> <\/span>\u2013 Building Agentic Systems for AI Agents<\/p><\/li> XenonStack Vision AI<\/span><\/a><\/b> <\/span>\u2013 Vision AI Platform<\/p><\/li> NexaStack AI<\/span><\/a><\/b> <\/span>\u2013 Inference AI Infrastructure for Agentic Systems<\/p><\/li><\/ul> Our mission is to accelerate the world\u2019s transition to <\/span>AI + Human Intelligence<\/b>, combining reasoning, perception, and action to create <\/span>enterprise\-ready AI agents<\/b>. We are seeking an <\/span>Agentic AI Engineer (Specialized in Reinforcement Learning)<\/b> <\/span>with <\/span>2\u20135 years of experience<\/b> <\/span>in applying RL to enterprise\-grade systems. This role involves designing and deploying <\/span>adaptive AI agents<\/b> <\/span>that continuously learn, optimize decisions, and evolve in dynamic environments. You\u2019ll work at the intersection of <\/span>RL research, agentic orchestration, and real\-world enterprise workflows<\/b> <\/span>\u2014 building agents that do more than automate, but truly <\/span>reason, adapt, and improve over time<\/b>. Reinforcement Learning Development<\/b> Design, implement, and train <\/span>RL algorithms<\/b> <\/span>(PPO, A3C, DQN, SAC) for enterprise decision\-making tasks. Develop <\/span>custom simulation environments<\/b> <\/span>to model business processes and operational workflows. Experiment with <\/span>reward function design<\/b> <\/span>to balance efficiency, accuracy, and long\-term value creation. Agentic AI System Design<\/b> Build <\/span>production\-ready RL\-driven agents<\/b> <\/span>capable of dynamic decision\-making and task orchestration. Integrate RL models with <\/span>LLMs, knowledge bases, and external tools<\/b> <\/span>for agentic workflows. Implement <\/span>multi\-agent systems<\/b> <\/span>to simulate collaboration, negotiation, and coordination. Deployment & Optimization<\/b> Deploy RL agents on <\/span>cloud and hybrid infrastructures<\/b> <\/span>(AWS, GCP, Azure). Optimize training and inference pipelines using <\/span>distributed computing frameworks<\/b> <\/span>(Ray RLlib, Horovod). Apply <\/span>model optimization techniques<\/b> <\/span>(quantization, ONNX, TensorRT) for scalable deployment. Evaluation & Monitoring<\/b> Develop pipelines for <\/span>evaluating agent performance<\/b> <\/span>(robustness, reliability, interpretability). Implement <\/span>fail\-safes, guardrails, and observability<\/b> <\/span>for safe enterprise deployment. Document processes, experiments, and lessons learned for continuous improvement. Technical Skills<\/b> 2\u20135 years of hands\-on experience with <\/span>Reinforcement Learning frameworks<\/b> <\/span>(Ray RLlib, Stable Baselines, PyTorch RL, TensorFlow Agents). Strong programming skills in <\/span>Python<\/b>; proficiency with <\/span>PyTorch / TensorFlow<\/b>. Experience designing and training <\/span>RL algorithms<\/b> <\/span>(PPO, DQN, A3C, Actor\-Critic methods). Familiarity with <\/span>simulation environments<\/b> <\/span>(Gymnasium, Isaac Gym, Unity ML\-Agents, custom simulators). Experience in <\/span>reward modeling and optimization<\/b> <\/span>for real\-world decision\-making tasks. Knowledge of <\/span>multi\-agent systems<\/b> <\/span>and collaborative RL is a strong plus. Familiarity with <\/span>LLMs + RLHF (Reinforcement Learning with Human Feedback)<\/b> <\/span>is desirable. Exposure to <\/span>cloud platforms (AWS/GCP/Azure)<\/b>, containers (Docker, Kubernetes), and CI/CD for ML. Professional Attributes<\/b> Strong analytical and problem\-solving mindset. Ability to balance <\/span>research depth<\/b> <\/span>with <\/span>practical engineering<\/b> <\/span>for production\-ready systems. Collaborative approach, working across AI, data, and platform teams. Commitment to <\/span>Responsible AI<\/b> <\/span>(bias mitigation, fairness, transparency). At XenonStack, we believe in <\/span>shaping the future of intelligent systems<\/b>. We foster a <\/span>culture of cultivation<\/b> <\/span>built on bold, human\-centric leadership principles, where <\/span>deep work, simplicity, and adoption<\/b> <\/span>define everything we do. Our Cultural Values<\/b> Agency<\/b> <\/span>\u2013 Be self\-directed and proactive. Taste<\/b> <\/span>\u2013 Sweat the details and build with precision. Ownership<\/b> <\/span>\u2013 Take responsibility for outcomes. Mastery<\/b> <\/span>\u2013 Commit to continuous learning and growth. Impatience<\/b> <\/span>\u2013 Move fast and embrace progress. Customer Obsession<\/b> <\/span>\u2013 Always put the customer first. Our Product Philosophy<\/b> Obsessed with Adoption<\/b> <\/span>\u2013 Making AI agents accessible and enterprise\-ready. Obsessed with Simplicity<\/b> <\/span>\u2013 Turning complex RL + agentic challenges into intuitive, reliable systems. Be part of our mission to <\/span>reimagine adaptive, enterprise\-grade AI agents<\/b> <\/span>with Reinforcement Learning and accelerate the world\u2019s transition to <\/span>AI + Human Intelligence<\/b>. <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p>
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<\/p>THE OPPORTUNITY<\/b>
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<\/p>JOB ROLES AND RESPONSIBILITIES<\/b>
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<\/p><\/li><\/ul>SKILLS REQUIREMENTS<\/b>
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<\/p><\/li><\/ul>XENONSTACK CULTURE \u2013 JOIN US & MAKE AN IMPACT!<\/b>
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<\/p>WHY SHOULD YOU JOIN US?<\/b>
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