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Design and deliver production-grade AI agents that are highly performant, reliable, and intuitive, driving revenue directly to Sierra's growth across industries like finance, healthcare, and commerce. Take complete ownership and autonomy in the Agent Development Life Cycle from initial pilot through deployment and continuous iteration, including building, tuning, and evolving AI agents in production environments while defining best practices. Work directly with leaders of large enterprises and cutting-edge startups to understand their business challenges and develop AI agents that transform their operations at scale. Guide the evolution of Sierra's core platform through customer interaction, surfacing unmet needs, prototyping new tools and features, and collaborating with research, product, and platform teams to shape AI agent development and Sierra's products.

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Build core primitives end to end including entity ownership, audit, authorization, and orchestration, ensuring the right actions are the default and incorrect actions are difficult. Own the domain model by turning Fluidstack's concepts of power, datacenters, and chips into composable entities that remain durable over time. Define interactions with external systems by interfacing with vendor systems and ingesting domain-specific formats such as KMZ, BIM, Revit, and vendor documents. Enable AI agents to be first-class operators of Fluidstack's systems by providing tools, guardrails, and audit trails to allow safe and effective operation beyond mere advice.

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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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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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In this role, you will build the next set of AI Features at Pylon, rapidly iterating based on customer feedback, and improve the quality and performance of AI features.

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Build the platforms that power Pylon's AI features such as prompt executions and search infrastructure. Improve LLM observability including AI evaluations both online and offline, scorers, and prepare Pylon's AI for future scaling. Enhance the quality and performance of AI features.

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Lead the team responsible for the automation, observability, configuration management, and policy enforcement layer that runs across the entire network fleet. Own the architecture, development, and production operation of the SDN Management Plane, including the automation and observability platform for managing network fleet across all regions. Build and operate CI/CD pipelines for network configuration, including automated testing, policy validation, and push-on-green delivery of network changes. Design and implement software systems that enforce reconciliation between declared and actual network state, detect configuration drift, and trigger automated remediation workflows. Define provisioning and onboarding automation for new nodes, regions, and customer environments. Drive the design of network observability systems such as streaming telemetry, synthetic probing, anomaly detection, and real-time traffic monitoring across GPU clusters. Design and implement self-healing network capabilities using closed-loop automation to detect, diagnose, and resolve network faults without human intervention. Set the technical vision for applying GenAI and machine learning to network operations. Partner with Control Plane and Data Plane teams to ensure software interfaces between layers and collaborate with infrastructure and compute teams to support GPU cluster networking requirements. Act as internal platform owner for network automation and treat engineering teams as customers with real product requirements. Lead, mentor, and grow a team of senior and staff-level software and network automation engineers, set technical standards, review architecture and design decisions, and own team performance and development. Foster a high-ownership engineering culture focused on shipping production software.

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Design and build extensible backend systems that support flexible configurations for different customers and content types. Develop infrastructure that interfaces cleanly with large language models (LLMs), enabling prompt engineering, context injection, and modular evaluation workflows. Build tooling and platforms that enable fast iteration by AI engineers and analysts, including declarative pipelines, parameterized jobs, and reproducible experiments. Prioritize ease of deployment, integration, and testing for both internal teams and external partners. Collaborate closely with product, data, and policy teams to translate nuanced safety needs into scalable, maintainable software systems.

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Debug and fix issues in the platform and ship PRs with fixes. Build internal tools and copilots powered by generative AI to support the team. Rapidly prototype proof-of-concepts for customer use cases. Collaborate across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.

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Design, build, and maintain scalable backend services and APIs that power Chattermill’s core analytics platform. Architect reliable, maintainable distributed systems and contribute to the evolution of backend service design and infrastructure. Own end-to-end delivery of backend engineering workstreams, from technical scoping and architecture through to implementation, testing, observability, and production support. Integrate language models, agentic frameworks, and AI pipelines into core product and backend services. Drive performance, reliability, and observability across high-throughput distributed data systems, including logging, tracing, alerting, and incident response. Work with cloud infrastructure and distributed systems in GCP (preferred) or AWS environments. Collaborate closely with Product to define scope, shape technical solutions, and explore new platform capabilities and features. Contribute to engineering excellence through code reviews, architectural discussions, and continuous improvement of development standards across the team.