• Automate the Azure infrastructure powering our products from end-to-end. • Harden what exists, modernize what needs it, and build new infrastructure the right way from the start using Bicep and ARM templates. • Build and mature our DataDog implementation including how we are using it, not just configuration. • Ensure our Azure environment, pipelines, and application follow security best practices. • Standardize what 'healthy' looks like for our systems. • Own the reliability and effectiveness of our Azure DevOps pipelines. • Build self-service tools, standards, and documentation. • Lead performance investigations and own post-incident reviews. • Act as a consultant and coach to development teams on cloud best practices, security, reliability patterns, and observability.

• Design and implement cloud-native architectures on AWS • Develop and maintain Infrastructure as Code (IaC) using tools like Terraform and TerraGrunt • Build and optimize Kubernetes clusters on Amazon EKS, including networking, storage, and security configurations • Implement and manage CI/CD pipelines using GitLab • Design and implement monitoring, logging, and alerting solutions using tools like Prometheus, Grafana, and AWS CloudWatch • Develop and maintain Helm charts for application deployments on Kubernetes • Implement security best practices and compliance measures across the platform • Automate operational tasks and create self-service tools for development teams • Collaborate with development teams to improve application deployment processes and platform usability • Continuously evaluate and integrate new technologies to enhance the platform's capabilities • Implement and maintain security controls in accordance with DoW standards such as NIST SP 800-53, DISA STIGs, and the DoW Enterprise DevSecOps Reference Design • Maintain and optimize AWS cloud infrastructure and cloud-based resources • Integrate DevSecOps tools and services to enable automated pipelines that support customer applications throughout their SDLC • Collaborate with development, operations, and security teams and contribute to the development of DevSecOps strategies and roadmaps • Develop and maintain comprehensive documentation for the platform, including user guides, API documentation, and best practices • Create engaging and informative diagrams to illustrate system architecture and workflows • Design and produce instructional resources such as tutorials, videos, and interactive learning materials • Develop and deliver training sessions to help users effectively leverage the platform • Adapt to evolving role responsibilities

• Own day-to-day platform operations across AWS infrastructure and Kubernetes environments. • Improve reliability, scalability, and performance of systems supporting our learning platform. • Operate and evolve multiple EKS clusters across production and non-production environments. • Build internal self-service tooling and engineering guardrails that help developers ship safely and independently. • Improve CI/CD workflows using GitHub Actions and GitOps practices with ArgoCD. • Drive observability improvements using DataDog, including dashboards, alerting, and incident response readiness. • Partner with software engineers, QA, product, and security teams to remove friction and increase delivery velocity. • Lead infrastructure changes end-to-end using Terraform and modern IaC practices. • Reduce operational toil through automation, standardization, and thoughtful platform design. • Participate in an on-call rotation focused on ownership, reliability, and continuous improvement. • Mentor teammates through collaboration, code review, and operational best practices.

Role Description We're seeking a Senior Staff Engineer to lead the Agent Platform work for Firefly Boards. Boards is bringing agentic AI to the infinite canvas, and this role owns the engineering systems that make that work production-grade: the server-side agent runtime, the integration with Adobe's central agent platform, and the application stack that lets the agent act on the canvas alongside human users in real time. You'll partner closely with the agentic team that owns the intelligence side of the product (skills, model selection, agent quality), with a deliberate split of responsibilities. They define what the agent does and how well it does it. You define the systems it runs on. Firefly Boards is an AI-powered mood-boarding and ideation app that provides a non-linear infinite canvas. It enables a new generation of creatives to embrace generative AI and those who find the linear workflows of legacy software restrictive. Images and videos can be imported or generated using AI models and edited, remixed, iterated on, and organized flexibly, making it easier for artists to visualize and develop ideas on their own or in collaboration. What You'll Do - Primary: - Server-side agent runtime: Stand up and own the service that runs Boards' agent workflows. State management, checkpointing, interrupt-and-resume across long-running operations, error recovery, and observability. - Browser/server execution boundary: Define and own how server-side workflows dispatch to browser-only tools (canvas operations, viewport state, selection) and resume cleanly. - Agent platform integration: Own the integration between Boards' agent runtime and Adobe's central agent platform. - Secondary: - Boards backend services for agentic consumption: Refactoring existing Boards APIs and adding new ones to be agent-callable. - Production-grade systems concerns: End-to-end observability and tracing for agent runs. How The Role Works This is a senior IC role, not a tech lead role. You set engineering direction for the agent platform work and partner across organizational boundaries to make the integration with Adobe's central agent platform succeed. The agentic team owns the intelligence side of the product — skills, prompts, tool design, model selection, and agent-quality evaluation. You own the systems they build on. Cross-team architecture is a real part of this role. You'll partner with Adobe's central agent platform team, the document services platform, model registry, identity, and security. Influence-without-authority across organizations is the operating mode. Qualifications - Bachelor's Degree or equivalent experience in Computer Science - 10+ years of product engineering experience, with significant ownership of production systems at scale - Demonstrated full stack depth: backend services and modern web client work in roughly equal measure. - JavaScript/TypeScript, declarative UI frameworks (React, Lit, Vue), and a strong server-side language - Distributed systems depth: Real experience with state management, checkpointing, async work, long-running operations, and recovery from partial failure. - Experience integrating with a central platform team as a tenant. - Experience designing service APIs and microservices, including REST or equivalent contracts that hold up across teams - Experience driving sophisticated software architecture, design, and development across multiple teams or organizations - Track record of writing clean, testable code and creating reusable components - Strong written and verbal communication. Preferred Qualifications - Experience building agentic or retrieval-augmented (RAG) systems with workflow engines such as LangGraph - Experience with real-time collaborative or multiplayer applications - Familiarity with MCP (Model Context Protocol) and agent tool design patterns - Experience with production LLM systems: evaluation infrastructure, observability, latency optimization - Experience with collaborative document models or CRDT-based systems - Experience building image, video, or multimedia applications - Experience with development tools such as Jenkins, GitHub, lint, etc. - Experience with improving performance and stability of large web applications - Experience with Accessibility (A11y) and Localization - Experience working in an Agile development environment Expected Pay Range The U.S. pay range for this position is $159,200 -- $301,600 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. In California, the pay range for this position is $208,300 - $301,600.