Senior Customer Facing Applied AI Engineer

About the role

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.

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.

Requirements

• 5+ years of software engineering experience with 1+ years working with ML/AI or LLM-based applications.
• A current understanding of the state of AI and are aggressively keeping up on the latest developments.
• Strong programming skills 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).
• 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.

Qualifications

• Exposure to LLM applications (RAG, agents, prompt pipelines) and their evaluation.
• Experience with logging/observability stacks (e.g., OpenTelemetry, Prometheus/Grafana, Datadog, etc.).
• Prior work in customer-facing technical roles or close collaboration with Customer Success / Solutions / Forward Deployed Engineering teams.
• Familiarity with MLOps/LLMOps concepts: evaluation harnesses, prompt/version management, feature flags, or canary rollouts.
• Proficiency in Java/Scala along with associated frameworks.

Skills

• Software engineering experience with 1+ years working with ML/AI or LLM-based applications.
• Current understanding of the state of AI and are aggressively keeping up on the latest developments.
• Strong programming skills 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).
• 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.
• Exposure to LLM applications (RAG, agents, prompt pipelines) and their evaluation.
• Experience with logging/observability stacks (e.g., OpenTelemetry, Prometheus/Grafana, Datadog, etc.).
• Prior work in customer-facing technical roles or close collaboration with Customer Success / Solutions / Forward Deployed Engineering teams.
• Familiarity with MLOps/LLMOps concepts: evaluation harnesses, prompt/version management, feature flags, or canary rollouts.
• Proficiency in Java/Scala along with associated frameworks.

Benefits

At Adobe, we aim to provide a competitive compensation package that includes a range of benefits to support your overall well-being. This role is eligible for bonus and equity.

Pay

The U.S. pay range for this position is $139,000 -- $257,550 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process.

Schedule

Our team is flexible and accommodating to ensure you can balance your work and personal life. We offer a hybrid schedule that allows for both remote and in-office work.