Sr. AI Systems Architect (ai/ml), Brand Concierge

Adobe Adobe · Enterprise · San Jose, CA

Senior AI Systems Architect role focused on designing and architecting agent-based workflows, data pipelines, and orchestration systems for enterprise applications. This involves translating business needs into scalable AI solutions, defining RAG architecture, authoring technical specifications for LLM-based solutions, and collaborating with ML engineers and product teams.

What you'd actually do

  1. Translate business goals into AI architecture: Collaborate with customers and team members to understand needs, assess feasibility, and define system scope.
  2. Design AI agents and orchestration workflows: Create end-to-end blueprints for multi-agent systems to support real-time, multi-turn interactions across business functions.
  3. Define data and RAG architecture: Specify data requirements and retrieval-augmented generation (RAG) configurations to ensure context-aware, grounded responses.
  4. Author technical specifications: Produce clear, detailed documentation for LLM-based solutions, including APIs, tools, prompt logic, and agent capabilities.
  5. Collaborate across teams: Work with ML engineers, product teams, and customer collaborators to align implementation with technical and business strategy.

Skills

Required

  • 10+ years in software architecture
  • enterprise AI engineering
  • Experience designing and deploying AI/LLM systems (e.g., OpenAI, Claude, RAG stacks, vector databases)
  • Strong understanding of conversational AI, agents, orchestration (LangChain, AutoGen, Semantic Kernel, etc.)
  • Deep understanding of modern ML techniques and the ML lifecycle from data gathering, training, model evaluation, MLOps, and productionizing models.
  • Familiarity with cloud platforms (AWS/GCP/Azure), containerization (Docker/K8s), and CI/CD
  • Excellent communication and collaborator engagement skills

Nice to have

  • multi-agent systems
  • tool calling
  • autonomous workflows
  • enterprise data governance
  • security
  • compliance
  • domain-specific AI solutions (e.g., finance, legal, HR, customer service)

What the JD emphasized

  • AI architecture
  • AI agents and orchestration workflows
  • data and RAG architecture
  • LLM-based solutions
  • ML engineers
  • enterprise AI engineering
  • AI/LLM systems
  • conversational AI, agents, orchestration
  • modern ML techniques
  • ML lifecycle

Other signals

  • designing and deploying AI/LLM systems
  • conversational AI, agents, orchestration
  • modern ML techniques and the ML lifecycle