Senior AI Engineer

Adobe Adobe · Enterprise · San Jose, CA

Senior AI Engineer at Adobe responsible for building and deploying end-to-end AI products, including GenAI applications, LLM-powered agents, and RAG systems. The role involves full lifecycle ownership from ideation with stakeholders to deployment and impact measurement, leveraging AI-assisted coding tools for rapid development on cloud infrastructure.

What you'd actually do

  1. Rapidly build end-to-end AI products — from idea to deployed application — using AI-assisted coding tools to accelerate development across the full stack (frontend, backend, data, infrastructure)
  2. Build and deploy GenAI applications — including LLM-powered agents, RAG systems, and AI assistants — to solve real business problems such as business intelligence, workflow automation, and Q&A over structured and unstructured data
  3. Build and maintain scalable ML/AI pipelines on cloud infrastructure (Azure, Databricks) that process large-scale user behavioral data
  4. Collaborate directly with business stakeholders across Adobe to identify high-impact AI opportunities, prototype solutions quickly, and deliver measurable results
  5. Own end-to-end delivery of AI services — from data ingestion and model training to API development, UI, deployment, monitoring, and business impact measurement

Skills

Required

  • Python
  • LLMs
  • prompt engineering
  • RAG
  • building AI agents
  • cloud platforms (Azure preferred, or AWS/GCP)
  • big data tools (Spark, Databricks)
  • machine learning
  • deep learning
  • statistical modeling
  • analytical and quantitative problem-solving ability
  • communication and collaboration skills

Nice to have

  • autonomous AI agents
  • multi-agent systems
  • agentic workflows
  • function calling
  • tool-use patterns
  • evaluation
  • observability for AI systems
  • building evals
  • monitoring hallucinations
  • measuring output quality at scale

What the JD emphasized

  • track record of shipping models or AI applications to production
  • Proficiency with AI-assisted coding tools (e.g., Claude Code, GitHub Copilot, Cursor) and a demonstrated ability to use them to rapidly build full-stack applications
  • Track record of using AI-assisted coding to rapidly ship production applications (e.g., building a working product in days, not months)

Other signals

  • building end-to-end AI products
  • shipping models or AI applications to production
  • building and deploying GenAI applications
  • collaborate directly with business stakeholders