Senior Forward Deployed Engineer

The Team: You will be joining the newly formed Forward Deployment Engineering (FDE) team within Adobe's Digital Experience organization. As part of a key innovation initiative, our mandate is to bridge the gap between product engineering and real-world implementation. We are a team of world-class AI and traditional engineers dedicated to solving the most complex challenges for the world's largest companies, ensuring they extract maximum value from the Adobe ecosystem.

The Opportunity: This is an opportunity to be a technical ambassador for Adobe. You will split your time between deep technical work and on-site collaboration with the engineering teams of global industry leaders. If you are passionate about customer empathy and technical excellence, this role offers the best of both worlds: the excitement of a startup-style environment with the resources and scale of Adobe.

Come help us build the future of digital experiences, one customer success story at a time.

What You'll Do:

• Implement A2A integration patterns (APIs, webhooks, event streams, connectors) so customers can plug our AI capabilities into their existing applications and workflows.
• Create reusable SDKs, templates, and reference implementations that reduce friction for customers adopting our AI features.
• 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.
  • Investigate failure modes, edge cases, and drift (e.g., low-quality responses, latency spikes, low adoption).
  • Segment metrics by customer, cohort, use case, or configuration to find patterns and opportunities.
• Design and maintain evaluation pipelines for AI features:
  • Define success metrics and guardrails.
  • Set up offline and online evaluation (test sets, acceptance thresholds, user rating flows, A/B tests).
• Instrument AI features with strong observability and testing:
  • Logging of inputs/outputs with privacy in mind.
  • Traces/timelines of model calls, retrieval steps, and downstream effects.
  • Dashboards and alerts for quality, performance, and usage.

• Design and implement AI-backed services and APIs in Python using PyTorch or similar frameworks
  .

• Work with customer-facing teams (Customer Success, Solutions, Forward Deployed) to:
  • Turn customer feedback and production data into prioritized improvements.
  • Provide clear, data-backed insights on what's working, what's not, and why.
• Join customer calls and workshops to:
  • Understand their systems, integration constraints, and success criteria.
  • Guide them on how best to use our APIs, SDKs, and observability tools.
• Act as a bridge between customer needs and internal product/engineering, ensuring what we build is usable, measurable, and scalable across many customers.

What You Bring:

• 8+ years of software engineering experience with 2+ years working with ML/AI or LLM-based applications
  .
• An inventive mind that is looking for the best solutions to the most interesting problems. This involves a build-first mentality that seeks answers in the practical application.
• A strong sense of mentorship and team building to grow everyone's roles.
• Current understanding of the state of AI and are aggressively keeping up on the latest developments
  . You should be able to articulate the benefits of different architectural options.
• Expertise in Python
  , including hands-on work with PyTorch or similar frameworks (Tensor Flow, JAX, etc.).
• Familiarity with A2A (application-to-application) integration patterns
  :
  • REST/gRPC APIs, webhooks, queues, or event-driven systems.
  • Authentication, rate limits, and basic reliability patterns.
• Comfortable with data-analyst-style work
  :
  • Writing SQL and working with analytics tools/notebooks.
  • Building or interpreting dashboards (e.g., metrics for model quality, latency, usage).
  • Turning data into clear, actionable recommendations.

• Be willing to dive into implementation with a customer to make sure they are maximizing their use of AI.

• Experience with at least one cloud platform (AWS / GCP / Azure) and standard dev tooling (Git, CI/CD, Docker).
• Exposure to LLM applications (RAG, agents, prompt pipelines) and their evaluation.
• Experience with logging/observability stacks…