About the job
Join our team as a Principal AI Systems Engineer and help shape the future of AI-powered engineering infrastructure. Design and orchestrate agentic systems, integrate LLMs, and build scalable, production-grade AI solutions. Collaborate across teams to drive innovation and operational excellence in a dynamic, growth-focused environment.
Responsibilities
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
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
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
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
Qualifications
Minimum
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
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