Docker AI Jobs

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Drive rapid engineering efforts to build full-stack applications using Generative AI tools, enabling deep exploration of product ideas, user experiences, and technical feasibility. Apply Generative AI building blocks such as prompt engineering, graph databases, vector databases, agentic frameworks, evaluations, and guardrails to real-world development. Make key decisions around infrastructure and platform choices, balancing short-term prototyping needs with long-term scalability. Collaborate cross-functionally with product, design, and AI experts to create, test, and iterate on new concepts. Use AI-assisted coding tools to enhance productivity and speed of iteration. Gather user feedback and iterate quickly based on insights to improve usability and effectiveness. Deploy and manage applications on cloud infrastructure including AWS, GCP, and Supabase. Build and integrate APIs and third-party services. Participate in architecture discussions and technical planning. Identify and troubleshoot issues across the stack. Contribute to improving development processes, tools, and team practices. Stay current with industry trends and emerging technologies.

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The responsibilities include translating business requirements into requirements for AI/ML models, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms especially transformers, testing and evaluating the AI/ML/DL models, publishing the models, datasets, and evaluations, deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines for models with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.

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Design and deliver production-grade AI agents that are highly performant, reliable, and intuitive, which are central and mission-critical to Sierra's growth across industries like finance, healthcare, and commerce. Take complete ownership and autonomy over the Agent Development Life Cycle (ADLC) from initial pilot through deployment and continuous iteration, including building, tuning, and evolving AI agents in production environments while defining best practices for ADLC. Partner with leaders at large enterprises and cutting-edge startups to understand their business challenges and build AI agents that transform their operations at scale. Collaborate with customers to guide the evolution of Sierra's core platform by surfacing unmet needs, prototyping new tools and features, and working with research, product, and platform teams to shape the future of AI agent development and Sierra's product.

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Architect and build next-generation system protections through hands-on design, model training, and deployment strategies. Lead and manage a small, senior team of Engineers, providing clear direction and autonomy. Collaborate with Research, Safety, Product, and Policy teams to use existing tools and advance new solutions. Utilize state-of-the-art models to detect and prevent problematic content. Establish evaluation frameworks and metrics to measure progress and identify improvement areas. Support team growth and maintain high performance through mentorship and career guidance.

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Build backend and infrastructure systems that extend OpenAI's API platform into cloud-native environments such as AWS. Design and ship cloud-contained products that allow customers to use OpenAI capabilities while keeping workloads and data within cloud environments. Help stand up cloud-hosted Codex experiences powered by the OpenAI Responses API. Build infrastructure and runtime abstractions for a stateful, cloud-optimized agentic platform. Partner closely with external cloud partners as well as internal teams across Codex, Research, and Safety Systems to translate emerging capabilities into production-ready systems. Improve the reliability, scalability, observability, and operational maturity of the services underpinning these products. Help shape the technical direction of a new and growing team as it scales from an early core group into a larger engineering organization. This role also involves building backend services, APIs, SDK integrations, authentication flows, and cloud service infrastructure that let developers use OpenAI capabilities in the cloud environments where they already build, and working across teams sometimes embedded with partner product groups to ship products quickly across multiple platforms at the same time.

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As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and the resilient cloud infrastructure for international government partners. You will own the production outcome, taking full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. You will ensure full-stack integrity by overseeing the end-to-end health of the platform and maintaining a responsive and production-ready environment. You will build automated systems to monitor model performance and data drift across geographically dispersed environments to ensure appropriate reliability. You will manage the technical lifecycle within diverse regulatory frameworks, lead the response for production issues in mission-critical environments, ensure rapid resolution, and build guardrails to prevent recurrence. You will translate deep technical performance metrics into clear insights for senior international government officials. Additionally, you will partner with Engineering and ML teams to incorporate lessons learned in the field into the technical architecture and decisions for future use cases.

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Translate business requirements into AI/ML model requirements. Prepare data to train and evaluate AI/ML/DL models. Build AI/ML/DL models using state-of-the-art algorithms, especially transformers, sometimes leveraging existing algorithms from research. Test and evaluate models, benchmark quality, and publish models, datasets, and evaluations. Deploy models in production by containerizing them. Work with customers and internal employees to refine model quality. Establish continuous learning pipelines for models with online or transfer learning. Build and deploy containerized applications on cloud or on-premise environments.

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Research Engineers design, prototype, and implement agentic AI systems that perform reliably across complex enterprise workflows. They build compound AI architectures combining planning, tool use, retrieval, memory, evaluation, orchestration, and execution. They investigate how agents reason, coordinate, recover from errors, and interact with external systems under real-world constraints. Research Engineers develop evaluation frameworks that measure agent behavior, task completion, reliability, robustness, and failure modes. They create tools and abstractions to make agent behavior easier to observe, debug, test, and improve. They partner with AI Researchers to explore new agent architectures and with AI Engineers to harden successful approaches for production use. They integrate agents into customer APIs, applications, data platforms, and operational workflows. They communicate clearly with internal teams and customer stakeholders about agent capabilities, limitations, tradeoffs, and risks.

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Build production AI workflows including agentic workflows over enterprise and government data with clear rules for model visibility, tool usage, and human review. Design context and grounding systems for models to provide relevant information without violating permissions or performance constraints. Work across backend services, APIs, async workers, data pipelines, internal tools, and product-facing surfaces. Engineer reliable LLM systems by building evaluations and feedback loops for model behavior and workflow outcomes. Own tracing and runtime visibility across models, context, tool calls, and generated outputs. Debug failures using context, traces, tool responses, user reviews, and production logs. Improve quality without compromising latency, cost, or security. Build reusable AI systems by creating shared primitives for context assembly, grounding, tool use, and reviewable outputs. Develop systems that turn domain-specific AI behavior into product infrastructure rather than one-off customer logic. Move quickly from prototype to production quality systems with founders and engineers. Lead through ownership and engineering quality by taking ownership of important product and platform surfaces without needing heavy direction. Write clean, maintainable code and create clear abstractions. Use tools like Claude Code, Codex, ChatGPT, Cursor, and similar to accelerate development while maintaining the same standards for generated and hand-written code. Treat LLMs as architectural components with failure modes and costs to manage, not just black boxes to call.

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Design, build, and launch end-to-end product experiences for image generation and image editing within ChatGPT. Develop highly interactive frontend experiences that make sophisticated AI capabilities feel intuitive, fast, and delightful. Build scalable backend services, APIs, and workflows that power image creation, editing, storage, sharing, and retrieval. Partner closely with researchers to rapidly prototype and productionize new multimodal capabilities. Collaborate with Product, Design, Data Science, and Engineering teams to identify high-impact opportunities and execute against them. Own projects from concept through launch, including technical design, implementation, experimentation, measurement, and iteration. Optimize performance across the stack, from frontend responsiveness and rendering to backend latency, reliability, and scalability. Design systems that can support millions of users generating and interacting with visual content simultaneously. Leverage experimentation and user insights to improve engagement, usability, quality, and product outcomes. Contribute to engineering best practices around architecture, testing, observability, developer productivity, and operational excellence. Help define the future roadmap for AI-powered creative tools and visual experiences.