Job Description & Details
This is a heavy-hitting engineering role bridging advanced machine learning with a massive, highly customized Adobe Commerce ecosystem. You won't just be playing in a Jupyter notebook; you'll be building production-grade AI features like search relevance tuning, intelligent recommendations, and automated workflows that directly impact a complex transactional platform. If you enjoy wrestling with real-world enterprise integrations and turning chaotic data flows into reliable microservices, this gig is worth a serious look.
What You'll Actually Be Doing
You'll spend your days designing, building, and deploying AI-powered applications that cleanly interface with Adobe Commerce and surrounding enterprise systems like ERP, OMS, and CRM. Your primary challenge is moving experimental AI workflows past the hype and into reliable production code—writing robust APIs, implementing strict guardrails and evaluation metrics, and ensuring everything scales nicely inside containerized environments. You will collaborate constantly with product teams and platform engineers to figure out what actually adds business value, all while adhering to enterprise governance and compliance standards.
The Core Tech Stack
You need to be completely fluent in Python for building production services, alongside enough PHP or JavaScript to understand how your APIs hook into the Adobe Commerce storefront. Hands-on experience with modern LLM orchestration frameworks, vector databases, and MLOps fundamentals—like model deployment, versioning, CI/CD, and Docker—is non-negotiable here. Because you'll be interacting with cloud hosting environments like AWS or Azure and integrating via REST or GraphQL, solid systems engineering chops are just as important as your machine learning background.
Interview Expectations
The engineering team is going to test your ability to build bulletproof AI architectures rather than just asking you textbook ML questions. Expect a deep dive into how you handle latency and failure modes when an LLM or vector search API goes down in the middle of a high-volume checkout flow. They want to see that you understand TDD for AI, so be ready to explain how you write meaningful evaluation tests for non-deterministic model outputs and how you manage strict data governance without slowing down feature delivery.
Application Advice
To get past the ATS, your resume needs to heavily emphasize production systems, scalability, and MLOps rather than just academic modeling or toy projects. Make sure keywords like Python, REST/GraphQL APIs, Docker, CI/CD, vector databases, and enterprise integrations are front and center. Frame your past experience around shipping reliable, well-tested microservices that solved real business problems in transactional or e-commerce environments.