Sr AI Machine Learning Engineer

About the Role

Qualifications, skills, and all relevant experience needed for this role can be found in the full description below.

At Petco , we are bridging the gap between cutting-edge AI research and real-world software applications. We are looking for a Machine Learning Engineer who acts as the bridge between Data Science and Software Engineering.

In this role, you will build the "smart" features that power the Petco experience—from personalized product recommendations and demand forecasting to automated price optimization. You won't just train models; you will write the scalable, production-grade code that ensures those AI models run smoothly and reliably on our website and mobile app without breaking.

What You'll Do (Day-to-Day)

• Model Development: Build the core AI models and algorithms that solve critical retail and e-commerce challenges (e.g., matching the right product recommendations to individual shoppers).
• MLOps & Automated Pipelines: Build and maintain robust data/ML pipelines. You will automate model re-training with daily data, monitor performance metrics in production, and ensure 24/7 reliability.
• Cross-Functional Integration: Partner closely with core Software Engineering and Mobile teams to seamlessly plug AI features directly into Petco's web and mobile platforms.
• R&D & Tech Innovation: Experiment with emerging technologies (e.g., Generative AI, LLMs) to discover new ways to drive revenue, automate processes, and enhance customer experience.
• Mentorship: Serve as a coach and technical guide for junior engineers, helping level up team practices and code quality.

What We're Looking For

• Experience: 3+ years of experience in Machine Learning Engineering, MLOps, or Software Engineering with an AI focus.
• Software Engineering Fundamentals: High proficiency in Python and standard engineering practices (clean code, OOP, CI/CD pipelines, Git, unit testing).
• Production MLOps: Proven track record of deploying and monitoring ML models in production (e.g., using MLflow, Airflow, Kubeflow, or cloud-native tooling).
• Cloud & Data Ecosystem: Strong experience with Cloud platforms ( AWS, GCP, or Azure ) and advanced SQL
• API & Systems Integration: Hands-on experience developing and consuming REST APIs or microservices to serve model predictions in real time.

Nice-to-Haves

• Practical experience or strong interest in Generative AI and LLM integration.
• Experience in E-commerce , Retail, or Supply Chain analytics. xhyhwjd
• Strong communication skills with an ability to translate complex technical ideas into business value.