Job description

About Us Dolby 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 Role Dolby 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 Do Own the AI systems architecture • Define 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 of • Establish integration patterns, credential management standards, and data access models that scale across teams and systems • Ensure the infrastructure is secure, observable, and operable — not just functional at launch Build and integrate across systems • Architect and implement the foundational data and integration layer that enables AI agents and applications to operate reliably across business functions • Build and maintain integrations between business systems — CRM, billing, support, product usage, and more — so that data flows where it needs to go • Collaborate with Product Engineering on shared infrastructure, systems integration, and platform capabilities — you know when to build and when to align • Configure and manage MCP servers and API connections to internal systems, with proper credential management and security controls • Stand up shared hosting infrastructure for agents, automations, and applications, moving work off local workstations and into production environments • Build scheduled automations and event-driven workflows that run reliably without manual intervention Deliver business solutions • Work with business stakeholders to understand problems, refine requirements, and define acceptance criteria — then own the build • Develop customer-facing and internal applications from requirements and user stories — these may be web applications, headless services, agentic flows, or a combination • Build AI-powered workflows for business teams including customer health monitoring, automated alerts, meeting intelligence, and revenue lifecycle automation • Iterate rapidly: deploy working software, collect feedback, and improve Raise the technical floor across DCS • Set the technical bar for how AI systems are built, integrated, and maintained across the organization — your decisions become the standard others follow • Maintain a shared repository of prompts, agents, skills, and workflows organized for reuse across teams • Help colleagues across Customer Success, Sales, Marketing, and Finance build and deploy their own AI solutions on the shared infrastructure What You Bring Required • 5+ 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 delivery • Proven experience making architectural decisions, set technical standards, and influenced how an engineering organization builds • Strong with Python or TypeScript/Node.js; comfortable picking up whatever the job requires • Experience building tool-use integrations for AI systems; familiarity with MCP (Model Context Protocol) or similar protocols/frameworks is strongly preferred • Proven experience integrating with SaaS APIs (REST, webhooks, OAuth) — you have connected real business systems and understand the edge cases • Hands-on experience with LLM APIs and agentic frameworks — you have built production agents or AI-powered workflows, not just prototypes • Comfortable working with business stakeholders — you can participate in requirements discussions, ask the right questions, push back constructively, and translate business intent into working software • Experienced collaborating with product and platform engineering teams — you know how to articulate what you need, align on approach, and get things built across team boundaries • Security-conscious: you understand data classification, access controls, API credentials, compliance requirements, and risk management principles, and you incorporate them into the systems you build Strong Plus • Experience designing federated data layers, shared integration infrastructure, or multi-system data pipelines • Familiarity with common SaaS business systems used in revenue, support, or customer success operations • Background in Customer Success, RevOps, or SaaS B2B — you understand the business domain you're building for • Experience incorporating data classification, compliance, security, and risk assessment considerations into the design and operation of AI-powered systems and business automations

Requirements

Must-haves

Own the AI systems architecture
Define 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 of
Establish integration patterns, credential management standards, and data access models that scale across teams and systems
Ensure the infrastructure is secure, observable, and operable — not just functional at launch
Build and integrate across systems
Architect and implement the foundational data and integration layer that enables AI agents and applications to operate reliably across business functions
Build and maintain integrations between business systems — CRM, billing, support, product usage, and more — so that data flows where it needs to go
Collaborate with Product Engineering on shared infrastructure, systems integration, and platform capabilities — you know when to build and when to align
Configure and manage MCP servers and API connections to internal systems, with proper credential management and security controls
Stand up shared hosting infrastructure for agents, automations, and applications, moving work off local workstations and into production environments
Build scheduled automations and event-driven workflows that run reliably without manual intervention
Deliver business solutions
Work with business stakeholders to understand problems, refine requirements, and define acceptance criteria — then own the build
Develop customer-facing and internal applications from requirements and user stories — these may be web applications, headless services, agentic flows, or a combination
Build AI-powered workflows for business teams including customer health monitoring, automated alerts, meeting intelligence, and revenue lifecycle automation
Iterate rapidly: deploy working software, collect feedback, and improve
Raise the technical floor across DCS
Set the technical bar for how AI systems are built, integrated, and maintained across the organization — your decisions become the standard others follow
Maintain a shared repository of prompts, agents, skills, and workflows organized for reuse across teams
Help colleagues across Customer Success, Sales, Marketing, and Finance build and deploy their own AI solutions on the shared infrastructure
Proven experience making architectural decisions, set technical standards, and influenced how an engineering organization builds
Strong with Python or TypeScript/Node.js; comfortable picking up whatever the job requires
Proven experience integrating with SaaS APIs (REST, webhooks, OAuth) — you have connected real business systems and understand the edge cases

Nice-to-haves

Experience building tool-use integrations for AI systems; familiarity with MCP (Model Context Protocol) or similar protocols/frameworks is strongly preferred

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigoapipythontypescriptnode.jsrestllm

Platform: eightfold
Effort: medium