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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Software Engineer, Cloud Architecture & Engineering Velocity - **Company:** Arc Full-time - **Location:** Washington, DC, United States - **Experience:** Expert - **Salary:** $131,500.0 - $219,100.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon Cloudfront, Software as a Service, Cloud Computing, Code Review, Databases, Continuous Integration, Customer Data Management, Cursor (Graphical User Interface Elements), Distributed Systems, Multi-Factor Authentication, Amazon DynamoDB, Identity and Access Management, Python (Programming Language), Nginx, Node.Js, OAuth, Systems Development Life Cycle, Cloud Services, Akamai, Security Assertion Markup Language (SAML), Systems Integration, Web Services, AWS Cdk, Okta, GitHub Copilot, Retrieval-Augmented Generation, Delivery Pipeline, State Machines, Cloudformation, Event Driven Architecture, AI Platforms, Infrastructure Automation Frameworks, Information Technology, Low Latency, Extreme Programming, Cloudflare, Api Design, Api Gateway, Terraform, Microservices - **Published:** September 24, 2026 - **Apply:** https://www.thejobnetwork.com/job/staff-software-engineer-cloud-architecture-engineering-velocity-509979357 ## About the Role * 8+ years designing and building highly scalable, distributed systems, including time as a technical leader or architect across multiple teams or domains. * Deep AWS expertise (Lambda, ECS, Step Functions, CloudFormation, IAM, DynamoDB) and strong CI/CD and infrastructure-as-code experience (Terraform, AWS CDK). * Proficiency in server-side languages, with a preference for Python and Node.js and experience in Go or Java. * Deep knowledge of identity protocols (SAML, SSO, MFA, OAuth) and familiarity with Okta, plus web service proxy and gateway components (NGINX, AWS API Gateway). * Strong grounding in API design, event-driven systems, microservices architecture, and database systems. * Hands-on experience integrating AI and large language model capabilities into production systems, including patterns such as retrieval-augmented generation, tool use, and agentic workflows, with an understanding of the cost and latency characteristics of inference. * Working knowledge of AI-assisted development tools (for example GitHub Copilot, Cursor, Claude Code) and how to govern their use within CI/CD and code review. * A bachelor's degree in computer science, engineering, or a related technical field is welcome. We weigh equivalent practical experience equally and do not require a degree., * Experience architecting AI-native platforms or AI gateway and governance layers in a multi-tenant SaaS environment. * Experience with Amazon Bedrock, SageMaker, or comparable managed AI services, and with the cost, latency, and quality tradeoffs across model providers. * Experience designing internal developer platforms and improving DORA or similar engineering-effectiveness metrics at scale. * Experience working with external or outsourced operations teams in a governed architecture model. * Experience in content platforms, publishing systems, or digital experience platforms. * Familiarity with CDNs and edge platforms such as Akamai, Cloudflare (Workers and related edge primitives), or CloudFront, and with edge intelligence and personalization patterns. * Experience with bot management, AI crawler and scraping defense, or agentic traffic monitoring and monetization at the edge (for example paid-crawl or metered-access models). * Experience defining FinOps practices for inference-heavy workloads, and with model, inference, and delivery-layer observability. Why Arc XP ## Description * Reduce lead time for change across the platform by owning Arc's AWS architecture and automation as a primary architect, spanning CI/CD and delivery pipelines, infrastructure as code, compute, data and event-driven patterns, service and API design, identity (SAML, SSO, MFA, OAuth, Okta, extended to agents), and gateway and proxy components (NGINX, AWS API Gateway, AI and inference gateways). * Give every team a fast, trusted path to production by establishing and evolving paved roads, reference architectures, and reusable components with signposts and guardrails built in. * Keep the platform performant, resilient, and affordable by driving architecture decisions for scale and cost (with particular attention to AI and edge workloads) and by owning production monitoring across model-level signals (latency, drift, failure modes) and delivery-layer signals (agentic traffic, crawler behavior, abuse). Developer Velocity & Platform Experience * Shorten the path from commit to production by removing bottlenecks across the delivery lifecycle and by designing self-service build, deploy, and scale capabilities that teams trust. * Let teams ship production-ready services in hours or days by designing developer platforms (CI/CD workflows, IaC frameworks such as AWS CDK and Terraform, service scaffolding, and observability patterns) with security and operational readiness built into the path, and by setting automation-first standards that cut manual toil. * Make effectiveness measurable by tracking lead time for changes, deployment frequency, change failure rate, and time to restore service, and by guiding the platform investments that move them. AI-Native Cloud Infrastructure * Keep the company safe as product teams build intelligent features by setting the cloud infrastructure patterns and guardrails for running AI and agentic workloads (inference across managed providers such as Amazon Bedrock and self-hosted models where appropriate, retrieval and vector storage, and model routing, fallback, and caching for latency, cost, and reliability). The emphasis is on structuring and protecting the infrastructure, not building the products. * Protect customer data as it flows through inference and agentic systems by architecting multi-tenant safety patterns: tenant-scoped retrieval, data isolation, prompt and response auditing, and abuse prevention. * Give Arc consistent control over which models are used, how, by whom, and at what cost by designing the AI gateway, governance, and observability layer, partnering with security and data leadership on residency, retention, and privacy, and developing the FinOps model for inference-heavy workloads. AI & the Experience Delivery Layer * Let Arc and its customers see who and what is consuming content by designing how we monitor AI and agentic traffic across the delivery path, from the edge through the content APIs. * Keep paid and proprietary content protected by architecting defenses against AI-driven attack patterns: aggressive crawlers, scraping, prompt injection, data exfiltration, and automated abuse that traditional bot management misses. * Turn agentic consumption into a business advantage by structuring the delivery architecture so Arc can safely meter, tier, and where appropriate monetize AI access, and by partnering with our CDN and edge providers (Akamai, Cloudflare, CloudFront) to make edge intelligence, bot and AI defense, and personalization first-class capabilities. AI-Augmented Engineering * Help teams adopt AI tooling with confidence by defining Arc's approach to AI-assisted development across the SDLC (coding assistants, code-review augmentation, test and IaC generation, incident-response copilots). * Hold AI-generated code to Arc's standards by establishing CI/CD guardrails and quality gates for security, correctness, and maintainability, and by building the evaluation harnesses that show where AI accelerates delivery and where it adds risk. Strategy, Governance & External Operations * Move the platform toward its future by guiding cross-organizational initiatives that make AI a core capability rather than a bolt-on, and by influencing leadership on tradeoffs and where to build, buy, or partner. * Keep external execution aligned with Arc's standards by acting as the primary technical authority for our external operations partners, establishing architectural guardrails, contracts, and validation, while partnering with internal SRE and operations and keeping a clean separation of concerns. * Raise the bar across the organization by mentoring senior engineers and architects, advocating for quality and testability (including evaluation strategies for AI behaviors), and embedding security best practices from prompt injection to data exfiltration and model misuse.