Kubernetes AI Jobs

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Translate business requirements into requirements for AI/ML models; prepare data to train and evaluate AI/ML/DL models; build AI/ML/DL models by applying state-of-the-art algorithms, especially transformers; leverage existing algorithms from academic or industrial research when applicable; test, evaluate, and benchmark the AI/ML/DL models, and publish the models, data sets, and evaluations; deploy models in production by containerizing the models; work with customers and internal employees to refine the quality of the models; establish continuous learning pipelines for models with online learning or transfer learning; build and deploy containerized applications on cloud or on-premise environments.

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Design and deploy production-grade agents using LangGraph and LangSmith that handle technical support queries, troubleshoot integrations, and guide users through complex onboarding flows. Analyze customer friction points to build self-service AI systems that reduce support volume and improve customer experience. Act as the product owner and technical lead to proactively identify opportunities for improvement, propose architectures, and own the full lifecycle of the systems built. Participate in the feedback loop for the product team by identifying gaps in frameworks and contributing to the LangChain and LangGraph open-source ecosystem. Develop AI-native onboarding workflows that automate documentation retrieval and code generation to help enterprise customers move from prototypes to production faster.

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Debug and fix issues in the platform and ship pull requests with those fixes. Build internal tools and copilots powered by generative AI to enhance the team’s capabilities. Rapidly prototype proof-of-concepts for customer use cases. Work collaboratively across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.

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Design, build, and maintain scalable machine learning systems including data ingestion, preprocessing, training, testing, and deployment. Develop and optimize end-to-end ML pipelines encompassing data collection, labeling, training, validation, and monitoring to ensure reliability and reproducibility. Implement robust MLOps practices such as model versioning, experiment tracking, CI/CD for machine learning, and continuous monitoring in production environments. Collaborate with product and engineering teams to integrate and deploy models into real-time products with a focus on efficiency and scalability. Ensure data quality, observability, and performance across all AI systems. Stay current with the latest AI infrastructure, tooling, and research to support ongoing innovation.

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Define the vision and roadmap for the Enterprise AI platform. Understand key enterprise use cases and pain points through deep engagement with forward deployed teams, turning common pain points into high leverage features. Partner with research, engineering, and design teams to translate AI capabilities into useful product features. Own the product lifecycle from ideation through launch.

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Serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows. Partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout. Build high-quality demos, reference implementations, and workflow automations, using Codex itself as part of the development process. Lead large-format workshops, technical deep dives, and hands-on enablement sessions that help engineering organizations adopt AI coding tools effectively and safely. Contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community accelerate their work with Codex. Gather high-fidelity product insights from real customer deployments and translate them into clear product proposals and model feedback for internal teams. Influence customer strategy and decision-making by framing how AI coding tools fit into their software development lifecycle, technical roadmap, and organizational workflows. Serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.

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Work on a small, high-caliber team building AI products for clients, from requirements gathering and prototyping through system design, development, testing, and deployment. Own features end-to-end and develop domain expertise across a range of AI use cases. Spend most of the time coding and frequently interact with clients to ensure the solutions meet their needs.

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You will work on a small, high-caliber team building AI products for clients, setting technical direction, writing code, and serving as the go-to person when challenges arise. Spend approximately 75% of your time coding and 25% interacting with clients, including CTOs, to understand problems, evaluate tradeoffs, and ensure solutions meet their needs.

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As a member of the Bots team, design, build, and scale systems that enhance user engagement with the AI-powered platform, including bot chat orchestration, AI image generation, AI video generation, and tooling for managing these features. Collaborate with cross-functional teams like product managers, designers, and data specialists to deliver high-quality, performant, and maintainable features. Experiment with and integrate new AI image, video, and voice generation technologies. Build tooling and infrastructure around various AI technologies. Gain exposure to the architecture and operations of a fast-growing social AI product. Contribute expertise to evolve team processes and technical infrastructure, ensuring scalability and reliability.

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Contribute to the technical development and integration of advanced robotic automation solutions for manufacturing automation, utilizing the Intrinsic platform, ROS and state-of-the-art AI capabilities. Collaborate closely with research and industry partners to successfully integrate AI and automation capabilities into factory settings. Document designs, processes, and results, communicating effectively with internal technical teams and partners.