ML Data Engineer

Job Description

Job Description What's the opportunity? We’re looking for a Machine Learning Data Engineer to enable data and ML capabilities that directly power the bank’s flagship Next Best Action (NBA) initiative, projected to deliver an incremental $160MM annual run-rate by FY28. As an ML Data Engineer, you will build and scale an AI-driven decisioning system that delivers hyper-personalized client experiences, ensuring the right action, at the right time, through the right channel, with the right offer, content, and placement. You will build AI agents that accelerate action creation and automate decisioning at scale, driving meaningful impact across millions of client interactions. You will work end to end across the ML lifecycle, from data and features through to deployed, monitored, and continuously improving models, bridging cutting-edge research and production systems to deliver measurable, AI-driven value. Your responsibilities include: Designing, building, and maintaining scalable data pipelines and feature stores that support end-to-end ML workflows for the NBA platform Collaborating with ML researchers and software engineers to productionize models and translate experimental approaches into reliable, high-performing systems Developing and deploying AI agents that automate workflow, reduce manual processes, and accelerate business process Owning the ML data lifecycle, from data ingestion, validation, and feature engineering to deployment, monitoring, and continuous optimization Ensuring data quality, reliability, governance, and performance at scale while enabling hyper-personalized, real-time client experiences You're our ideal candidate if you have: Bachelor’s degree in Computer Science, Software Engineering, or a related field, with 3 years of professional experience as a data or software engineer Proficiency in Python and Java, with hands-on experience using modern data and ML tooling (e.g., Spark, Airflow, feature stores, ML platforms) Strong foundation in both data and software engineering, designing and building scalable data pipelines and ML-ready datasets in hybrid environments spanning on-prem infrastructure and public cloud platforms (i.e. AWS) A solid understanding of the ML data lifecycle, including feature engineering, model integration, deployment support, and monitoring Experience building or enabling AI-driven automation (e.g., agents, workflow orchestration, or decision engines) that reduces manual effort Experience with DevOps and CI/CD tooling such as Jenkins and GitHub Actions to automate testing, builds, and deployments for data and ML pipelines Excellent collaboration and communication skills, with the ability to translate complex technical ideas into practical, business-focused solutions What's in it for you? Become part of a team that thinks progressively and works collaboratively. We care about seeing each other reach full potential. A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive c

Required Skills