Sr. AI Systems Engineer- Agentic and Productivity Systems

The Opportunity The Creative Cloud Engineering organization is building the next generation of AI-powered engineering infrastructure to accelerate developer productivity and operational excellence across the Creative Cloud ecosystem. As we expand into AI-driven workflows across developer productivity and platform initiatives, we are looking for a Senior AI Systems Engineer who operates at the intersection of experimentation and production systems. This role focuses on designing, orchestrating, and operationalizing agent-based systems that improve engineering workflows across CI/CD, developer tooling, and operational diagnostics. This is not a research role and not a prompt-engineering role. This is a systems engineering role focused on building durable infrastructure. You will help build AI-native engineering capabilities that compound engineering velocity across Creative Cloud over time. What You'll Do Agentic Workflow Development · Design and prototype agent-based systems for engineering workflows such as CI diagnostics, code review automation, build failure triage, and autonomous debugging · Develop multi-agent orchestration patterns with structured state, memory, and control boundaries · Rapidly evaluate emerging AI frameworks, agent tooling, and developer AI platforms in real-world engineering environments AI Systems Infrastructure · Build reusable orchestration layers and service architectures for AI-powered engineering systems · Develop structured evaluation pipelines including trace-based evaluation and regression testing for agent behavior · Implement feedback loops and instrumentation that continuously improve AI system performance Production Hardening · Convert experimental workflows into secure, scalable, production-grade services · Implement observability, tracing, cost controls, and model routing · Ensure reliability, operational stability, and measurable impact of AI-powered systems Platform Strategy & Collaboration · Define internal standards for AI experimentation, evaluation, deployment, and monitoring · Partner with DevEx, CI/CD, and platform teams across Creative Cloud to embed AI-native capabilities · Build cohesive infrastructure that prevents tool sprawl and enables reusable AI productivity systems across teams What Success Looks Like · Production-grade AI agents integrated into engineering workflows and CI systems · A standardized evaluation and tracing framework adopted across Creative Cloud engineering teams · Measurable reductions in manual debugging, failure triage, and operational friction · Reusable AI infrastructure components leveraged across multiple engineering teams · A clear AI productivity roadmap aligned with Creative Cloud platform initiatives Required Qualifications · 8 years of software engineering experience, with demonstrated depth in systems-level work · Strong systems engineering experience (Python, Go, TypeScript, or similar) · Experience building distributed systems, developer platforms, or infrastructure services · Experience integrating LLMs or AI APIs into production systems · Experience evaluating and integrating across multiple AI providers (e.g., AWS Bedrock, Anthropic, OpenAI) including cost optimization and capacity planning · Strong understanding of observability, metrics, logging, and tracing systems · Experience operating production services at scale Preferred Qualifications · Experience with agent frameworks (LangGraph, AutoGen, CrewAI, or similar) · Experience with embeddings, vector databases, or RAG architectures · Experience designing evaluation and benchmarking systems for AI workflows · Experience with CI/CD platforms, developer tooling, or build systems · Experience building internal developer productivity platforms · Familiarity with cost-aware model orchestration and multi-model routing Ideal Candidate Profile · Has built and shipped an AI-powered system end-to-end, not just integrated an API · Can show a prototype the ... (truncated, view full listing at source)