Staff Software Engineer, AI Systems

Via Licensing Corporation; Atlanta (GA); On-site; USD 150,000 - 210,000; Full time

Job summary

Dolby Cloud Solutions in Atlanta is seeking a Staff-level engineer to own the AI infrastructure powering its AI-driven workflows across business functions. You will define patterns, standards, and integrations, collaborating with Product Engineering to deliver secure, observable, production-ready AI solutions at scale.

Ideal candidates have strong Python or TypeScript, SaaS API experience, and a focus on data security, translating business needs into reliable software and automated workflows for

Qualifications

• 5+ years building production software with AI-assisted development tools as a core delivery factor.
• Experience making architectural decisions and setting technical standards.
• Experience integrating with SaaS APIs (REST, webhooks, OAuth).
• Hands-on with LLMs and agentic workflows in production.

Responsibilities

• Own the AI systems architecture and define scalable patterns.
• Build and maintain integrations between business systems and data flows.
• Collaborate with Product Engineering on shared infrastructure and platform capabilities.
• Stand up hosting infrastructure for agents, automations, and apps.
• Deliver AI-powered workflows across business teams and automate processes.

Skills

AI systems architecture

Python

TypeScript/Node.js

SaaS APIs integration

Security and compliance

Stakeholder collaboration

Tools

MCP

REST

OAuth

LLM APIs

Agent frameworks

Job description

Job description Company and benefits Social Impact & SustainabilityJob ID41663About UsDolby Cloud Solutions is a video streaming software company that helps enterprise and mid-market organizations deliver exceptional audio and video experiences to their audiences. The Dolby OptiView SaaS platform serves a growing portfolio of customers across media, advertising, and real-time communications — with a mission to make immersive video experiences accessible and scalable for everyone.About This RoleDolby Cloud Solutions is embedding AI into the core of how it operates — not as a side project, but as a fundamental shift in how work gets done across the business. The philosophy guiding this work is simple: AI prepares, humans decide.This is a Staff-level engineering role at the center of that effort. You will own the technical architecture of the shared AI infrastructure that DCS teams build their business processes on — and you will build specific solutions on top of it. You will define the patterns, standards, and integration approaches that others follow, and serve as the technical authority when architectural decisions need to be made.Business problems are owned by business stakeholders. You will work closely with those stakeholders — helping shape requirements and acceptance criteria where needed, then owning the build end to end. You will also collaborate with OptiView Product Engineering on shared infrastructure and systems integration, and engage Product Engineering when platform features or APIs need to be developed to support the work.What You'll DoOwn the AI systems architectureDefine the technical architecture of the shared data and integration layer — not just build it, but set the standards and patterns that others build on top ofEstablish integration patterns, credential management standards, and data access models that scale across teams and systemsEnsure the infrastructure is secure, observable, and operable — not just functional at launchBuild and integrate across systemsArchitect and implement the foundational data and integration layer that enables AI agents and applications to operate reliably across business functionsBuild and maintain integrations between business systems — CRM, billing, support, product usage, and more — so that data flows where it needs to goCollaborate with Product Engineering on shared infrastructure, systems integration, and platform capabilities — you know when to build and when to alignConfigure and manage MCP servers and API connections to internal systems, with proper credential management and security controlsStand up shared hosting infrastructure for agents, automations, and applications, moving work off local workstations and into production environmentsBuild scheduled automations and event-driven workflows that run reliably without manual interventionDeliver business solutionsWork with business stakeholders to understand problems, refine requirements, and define acceptance criteria — then own the buildDevelop customer-facing and internal applications from requirements and user stories — these may be web applications, headless services, agentic flows, or a combinationBuild AI-powered workflows for business teams including customer health monitoring, automated alerts, meeting intelligence, and revenue lifecycle automationIterate rapidly: deploy working software, collect feedback, and improveRaise the technical floor across DCSSet the technical bar for how AI systems are built, integrated, and maintained across the organization — your decisions become the standard others followMaintain a shared repository of prompts, agents, skills, and workflows organized for reuse across teamsHelp colleagues across Customer Success, Sales, Marketing, and Finance build and deploy their own AI solutions on the shared infrastructureWhat You BringRequired5+ years building production software — applications, APIs, backend services, platform infrastructure, or data integrations — with a demonstrated track record of leveraging AI-assisted development tools (Devin, Claude, Codex, Cursor, or similar) as a core part of engineering deliveryProven experience making architectural decisions, set technical standards, and influenced how an engineering organization buildsStrong with Python or TypeScript/Node.js; comfortable picking up whatever the job requiresExperience building tool-use integrations for AI systems; familiarity with MCP (Model Context Protocol) or similar protocols/frameworks is strongly preferredProven experience integrating with SaaS APIs (REST, webhooks, OAuth) — you have connected real business systems and understand the edge casesHands-on experience with LLM APIs and agentic frameworks — you have built production agents or AI-powered workflows, not just prototypesComfortable working with business stakeholders — you can participate in requirements discussions, ask the right questions, push back constructively, and translate business intent into working softwareExperienced collaborating with product and platform engineering teams — you know how to articulate what you need, align on approach, and get things built across team boundariesSecurity-conscious: you understand data classification, access controls, API credentials, compliance requirements, and risk management principles, and you incorporate them into the systems you buildStrong PlusExperience designing federated data layers, shared integration infrastructure, or multi-system data pipelinesFamiliarity with common SaaS business systems used in revenue, support, or customer success operationsBackground in Customer Success, RevOps, or SaaS B2B — you understand the business domain you're building forExperience incorporating data classification, compliance, security, and risk assessment considerations into the design and operation of AI-powered systems and business automations