Lead Customer Facing Applied AI Engineer

Adobe Adobe · Enterprise · San Jose, CA +2

Lead Customer Facing Applied AI Engineer at Adobe responsible for implementing AI integration patterns, creating reusable SDKs, acting as a data analyst for model behavior, designing evaluation pipelines, instrumenting AI features with observability, and designing/implementing AI-backed services. The role involves deep technical work and on-site collaboration with global industry leaders to ensure they extract maximum value from Adobe's AI capabilities.

What you'd actually do

  1. Implement A2A integration patterns (APIs, webhooks, event streams, connectors) so customers can plug our AI capabilities into their existing applications and workflows.
  2. Create reusable SDKs, templates, and reference implementations that reduce friction for customers adopting our AI features.
  3. Act like a data analyst for model behavior: Query logs and metrics (SQL, notebooks, dashboards) to understand how models and prompts are performing in production.
  4. Design and maintain evaluation pipelines for AI features: Define success metrics and guardrails.
  5. Instrument AI features with strong observability and testing: Logging of inputs/outputs with privacy in mind.

Skills

Required

  • Python
  • PyTorch or similar frameworks
  • A2A integration patterns
  • REST/gRPC APIs
  • webhooks
  • queues
  • event-driven systems
  • SQL
  • analytics tools/notebooks
  • dashboards
  • cloud platform (AWS / GCP / Azure)
  • Git
  • CI/CD
  • Docker
  • LLM applications (RAG, agents, prompt pipelines)
  • evaluation harnesses
  • prompt/version management
  • feature flags
  • canary rollouts
  • logging/observability stacks
  • OpenTelemetry
  • Prometheus/Grafana
  • Datadog
  • communication skills

Nice to have

  • Java/Scala
  • customer-facing technical roles
  • Marketing or CRM domain knowledge
  • Published technical writing
  • SaaS software background

What the JD emphasized

  • 8+ years of software engineering experience with 2+ years working with ML/AI or LLM-based applications
  • aggressively keeping up on the latest developments
  • shipping production features
  • usable, measurable, and scalable

Other signals

  • customer-facing
  • implementation
  • evaluation
  • observability
  • scalability