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Signet Jewelers is the world's largest retailer of diamond jewelry, operating more than 2,800 stores worldwide under the iconic brands: Kay Jewelers, Zales, Jared, H.Samuel, Ernest Jones, Peoples, Banter by Piercing Pagoda, Rocksbox, JamesAllen.com and Diamonds Direct. We are a people-first company and this core value is at the heart of everything we do, from empowering our valued team members, to collaborating with our customers, to fostering the communities in which we live and serve. People – and the love their actions inspire – are what drive us. We’re not only proud of the love we inspire outside our walls, we’re especially proud of the diversity, inclusion and equity we’re inspiring inside. There are dynamic career paths awaiting you – rewarding opportunities to impact the lives of others and inspire love. Join us!
We are looking for a hands-on Machine Learning Engineer to operationalize advanced models across elasticity, uplift, forecasting, and other AI use cases. This role sits at the intersection of Data Engineering, MLOps, and Applied Machine Learning, ensuring that models developed by Data Science teams are production-ready, performant, and scalable.Key Responsibilities
Design, build, and automate production-grade data pipelines to support elasticity, uplift, and other analytical models
Implement clean, reusable data transformation logic that ensures consistency across modeling, analytics, and reporting layers
Develop and maintain real-time inference services (e.g., AWS SageMaker endpoints) to allow business teams and applications to consume model outputs seamlessly
Establish MLOps best practices, including:
Model performance monitoring and drift detection
Automated retraining and evaluation pipelines
Feature / model versioning and lineage tracking
Enable CI/CD for ML deployments, ensuring reliability, reproducibility, and rapid iteration
Partner with Data Science teams to accelerate experimentation and automate recurring workflows
Identify and drive automation opportunities across the broader AI & Data Science ecosystem to improve scalability, reliability, and cost efficiency
Preferred Qualifications
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Applied ML, or equivalent experience
3–6 years of industry experience in ML Engineering or MLOps
Experience in retail analytics — such as demand forecasting, pricing, promotions, inventory optimization, customer segmentation, or e-commerce metrics — is highly preferred
Strong programming skills in Python (pandas, PySpark, FastAPI, etc.)
Experience building and managing ETL/ELT pipelines
Hands-on experience deploying ML systems on AWS (SageMaker, Lambda, ECS/EKS, S3, Kinesis/Streams, etc.)
Experience with CI/CD tools (GitHub Actions, CodePipeline, Jenkins, etc.)
Familiarity with monitoring and observability for ML (model drift, feature drift, inference latency, cost monitoring)
Experience with containerization & orchestration (Docker, Kubernetes) is a plus
Nice to Have
Experience building data products or ML-powered APIs that expose predictions or insights back to business applications.
Experience with feature stores (SageMaker Feature Store / Feast)
Experience supporting batch + real-time inference workloads
Who You Are
You enjoy solving problems at the intersection of data + ML + production systems
You care deeply about scalability, automation, and reliability
You love partnering with Data Scientists and Analysts to turn prototypes into products
BENEFITS AND PERKS:
Comprehensive healthcare, dental, and vision insurance to keep you and your family covered
Generous 401(k) matching after just one year to help secure your financial future
Ample paid time off, plus seven holidays to recharge and unwind
Exclusive discounts on premium merchandise just for you
Dynamic Learning & Development programs to support your growth
And more!