Senior AI Engineer
Explicitly requires vibe-coding skills and AI-assisted coding; expects rapid prototyping with tools like Claude Code, Copilot, and Cursor to build LLM agents and RAG systems.
About the Role
Senior AI Engineer role at Adobe to design, build, and ship end-to-end AI products—from LLM-powered agents and RAG systems to scalable ML pipelines—using AI-assisted coding and cloud infrastructure to deliver measurable business impact across multiple Adobe business units.
Job Description
Role
Adobe is hiring a Senior AI Engineer to build end-to-end AI products that drive business outcomes. The role covers the full lifecycle: identifying opportunities with stakeholders, rapid prototyping using AI-assisted coding, building frontend/backends and data pipelines, deploying models and services to production, and measuring business impact.
Key Responsibilities
- Rapidly prototype and ship full-stack AI products using AI-assisted coding tools to accelerate development.
- Build and deploy GenAI applications, including LLM-powered agents, AI assistants, and RAG systems for BI, automation, and question-answering over structured and unstructured data.
- Design, build, and maintain scalable ML/AI pipelines on cloud infrastructure that process large-scale user behavioral data.
- Collaborate with business stakeholders to identify high-impact AI opportunities, prototype solutions quickly, and deliver measurable results.
- Own end-to-end delivery of AI services: data ingestion, model training, API development, UI, deployment, monitoring, and business-impact measurement.
Requirements
- MS or PhD in Computer Science, Statistics, Machine Learning, or related field; or BS with equivalent practical experience.
- 5+ years’ experience building ML/AI systems with a track record of shipping models or AI applications to production.
- Strong programming skills in Python.
- Proficiency with AI-assisted coding tools (examples called out: Claude Code, GitHub Copilot, Cursor) and demonstrated ability to use them for rapid full-stack development.
- Hands-on experience with LLMs, prompt engineering, RAG, or building AI agents.
- Experience with cloud platforms (Azure preferred; AWS/GCP acceptable) and big data tools (Spark, Databricks).
- Solid foundation in machine learning, deep learning, and statistical modeling.
- Strong analytical and quantitative problem-solving abilities and excellent communication/collaboration skills for working with non-technical stakeholders.
Preferred Qualifications
- Experience building autonomous AI agents, multi-agent systems, or agentic workflows that orchestrate tools and APIs.
- Experience with function-calling and tool-use patterns for connecting LLMs to external systems and data sources.
- Experience building evaluation and observability for AI systems (e.g., evals, monitoring hallucinations, measuring output quality at scale).
- Demonstrated track record of using AI-assisted coding to rapidly ship production applications (e.g., building working products in days).
Compensation
- U.S. pay range listed: $142,700 - $270,950 annually.
- California-specific range listed: $187,100 - $270,950 annually.