Senior Forward Deployed Engineer

Adobe•San Jose, CA

8d•Hybrid

About The Position

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. 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.

Requirements

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 (TensorFlow, 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 (e.g., OpenTelemetry, Prometheus/Grafana, Datadog, etc.).
Familiarity with MLOps/LLMOps concepts: evaluation harnesses, prompt/version management, feature flags, or canary rollouts.
Proficiency in Java/Scala, along with associated frameworks.
Proven track record of shipping production features and iterating based on real-world feedback.
Strong communication skills with the ability to explain technical details and data insights to customers and non-ML stakeholders.
Comfortable with 10%-25% travel to build relationships with customers

Nice To Haves

Published technical writing or other communication
A background in SaaS software
Prior work in customer-facing technical roles or close collaboration with Customer Success / Solutions / Forward Deployed Engineering teams.
Marketing or CRM domain knowledge

Responsibilities

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.