Job description :

Role : Machine Learning Engineer

Role summary :

We are seeking a Machine Learning Engineer to build, deploy, and operate scalable, production-ready machine learning systems that support critical retail business capabilities across merchandising, supply chain, pricing, personalization, and digital commerce.

This role focuses on operationalizing ML models at scale, ensuring they are reliable, performant, and continuously improving in real-world environments.

You will own key aspects of the ML lifecycle, including feature engineering, model training and evaluation, deployment, monitoring, and retraining

Key responsibilities :

ML System Design and Development :

- Design, implement, and deploy ML models for use cases such as demand forecasting, replenishment, inventory optimization, assortment planning, and pricing/markdown optimization.

- Build batch and streaming data pipelines that connect POS, ecommerce, store operations, DCs, and external signals (promotions, events, weather, holidays).

- Collaborate with application and platform engineers to integrate ML outputs into downstream systems (store tools, supplychain systems, merchandising tools, digital experiences).

MLOps and reliability :

- Implement and maintain MLOps workflows: automated training, CI/CD for ML, model registration and versioning, experiment tracking, and scheduled retraining.

- Set up monitoring for data quality, model performance, and drift; define alert thresholds and incident playbooks.

- Optimize ML systems for cloud cost, runtime performance, and scalability, balancing business needs with technical constraints.

- Contribute to shared ML tooling, templates, and best practices, enabling other teams to build on a common platform.

Collaboration and technical leadership :

- Partner with data scientists on feature engineering, model selection, and evaluation, bringing a strong engineering perspective to ensure models can succeed in production.

- Work closely with product and operations stakeholders to understand requirements, translate them into technical tasks, and communicate model performance and limitations clearly.

- Document systems and processes (architecture, data flows, runbooks, dashboards) so that models are operable by teams across time zones.

Minimum qualifications :

- Bachelors or Masters degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field.

- 57 years of experience as a Machine Learning Engineer or similar role, with a track record of deploying and operating ML models in production systems.

- Strong proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, and scikitlearn.

- Experience using Databricks AutoML, MLOps, Agent Bricks and Delta Tables.

- Strong understanding of ML concepts: supervised and unsupervised learning, timeseries forecasting, evaluation metrics, and model monitoring.

- Experience deploying ML workloads on cloud platforms (AWS/Azure/GCP) using containers and/or orchestration frameworks.

- Good communication and collaboration skills, comfortable working with distributed teams and nontechnical stakeholders.

Nice to have :

- Experience in retail, ecommerce, logistics, or supply chain domains.

- Experience with recommendation systems, personalization, marketing/CRM models (propensity, churn, LTV), or inventory optimization.

- Familiarity with using ML systems in combination with LLM/AI applications (e.g., ranking, routing, eligibility, or guardrail models around generative systems)