Senior AI Platform Engineer

Adobe Adobe · Enterprise · Seattle, WA +2

Senior AI Platform Engineer at Adobe Express, focusing on building and scaling the core AI platform infrastructure for creative experiences. The role involves developing backend services, data and inference pipelines, and runtime systems for model integration, evaluation, fine-tuning, and deployment.

What you'd actually do

  1. Contribute to the development of core platform components that support AI experiences in Adobe Express.
  2. Build and improve backend services, microservices, and workflows that connect models, APIs, data systems, and product features.
  3. Help develop data and inference pipelines for training, evaluation, fine-tuning, and deployment of ML models.
  4. Support runtime systems for inference and orchestration with attention to reliability, observability, and performance.
  5. Work on storage, caching, and data-access patterns to improve efficiency, scalability, and cost.

Skills

Required

  • 3+ years of experience in software engineering, backend infrastructure, data systems, ML infrastructure, or related areas.
  • Good understanding of distributed systems fundamentals, backend services, and scalable system design.
  • Experience building or supporting APIs, data pipelines, or event-driven systems.
  • Proficiency in Python, Typescript, C++, or Go.
  • Familiarity with cloud environments, service deployment, and production engineering practices.

Nice to have

  • Exposure to ML systems or LLM-based applications, including model inference, orchestration, or evaluation, is a plus.
  • Strong problem-solving skills and the ability to work well in a collaborative team environment.
  • Clear communication skills and willingness to learn from cross-functional partners.
  • Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Data Science, or a related technical field.
  • Experience with technologies such as Kafka, Spark, Flink, or similar distributed data frameworks.
  • Exposure to generative AI systems such as LLMs, multimodal models, or diffusion models.
  • Familiarity with MLOps concepts such as experiment tracking, model deployment, or evaluation workflows.
  • Interest in agentic AI concepts such as tool use, task planning, or memory systems.

What the JD emphasized

  • core AI platform
  • AI infrastructure
  • production systems
  • model integration
  • inference services
  • data pipelines
  • storage and caching systems
  • analytics
  • evaluation tooling
  • backend services
  • microservices
  • data systems
  • product features
  • inference pipelines
  • evaluation
  • fine-tuning
  • deployment of ML models
  • runtime systems
  • inference
  • orchestration
  • reliability
  • observability
  • performance
  • storage
  • caching
  • data-access patterns
  • efficiency
  • scalability
  • cost
  • production-ready AI capabilities
  • debugging
  • testing
  • monitoring
  • operational improvements
  • ML infrastructure
  • scalable system design
  • data pipelines
  • event-driven systems
  • cloud environments
  • service deployment
  • production engineering practices
  • ML systems
  • LLM-based applications
  • model inference
  • orchestration
  • evaluation
  • MLOps concepts
  • experiment tracking
  • model deployment
  • evaluation workflows
  • agentic AI concepts
  • tool use
  • task planning
  • memory systems

Other signals

  • build production systems
  • scale AI infrastructure
  • support modern AI products