Lead Customer Facing Applied AI 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 (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

Desired Extras:

• 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