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The Machine Learning Engineer role involves owning ML projects end-to-end, designing production ML systems, and leveraging AI tools to automate decision-making. Responsibilities include building robust pipelines, communicating with stakeholders, and mentoring team members, especially for Senior candidates.

Architects, develops, and deploys high-performance machine learning systems across the full lifecycle. Responsibilities include large-scale feature engineering, online and offline pipelines, tree-based and deep learning models, low-latency serving, MLOps automation, model monitoring, and rigorous experimentation. The role requires production-grade software engineering, distributed computing, statistical expertise, and experience shipping ML systems in high-scale environments.

Architects, develops, and deploys high-performance machine learning systems across the full ML lifecycle. Responsibilities include building large-scale data and feature pipelines, training tree-based and deep learning models, developing low-latency inference services, implementing MLOps and monitoring, and designing rigorous experimentation. The role requires strong Python and software engineering expertise, distributed computing experience, production ML serving skills, and familiarity with statistical evaluation and A/B testing.

Owns product-area design from research and ideation through prototyping, launch, and iteration. Partners with product and engineering, conducts usability testing, maintains design systems, and uses AI tools for research synthesis, prototyping, UX copy, accessibility, and QA. The role requires product and business judgment, strong Figma craft, AI product design expertise, code literacy, independent execution, and clear stakeholder communication.

Owns product-area design from research and prototyping through release and iteration. Partners with product and engineering, conducts usability testing, maintains design systems, and uses AI tools for rapid prototyping, research synthesis, UX copy, accessibility, and QA. Designs trustworthy AI and agentic workflows while balancing user needs, business metrics, technical feasibility, and delivery constraints. Shares AI workflows and ensures quality across all shipped work.

Owns end-to-end UX/UI design for a product area, from research and rapid AI-assisted prototyping through usability testing, launch, and iteration. Partners with product and engineering, maintains accessible design systems, evaluates AI-generated designs, and connects design decisions to business outcomes. Requires strong Figma craft, AI design-tool fluency, product thinking, design research, communication, and enough coding literacy to work with front-end components and AI coding tools.

Designs and operates production machine learning systems on Azure, including training, deployment, monitoring, retraining, governance, and retirement workflows. Productionizes data science prototypes into deployable Python services, builds CI/CD pipelines, manages MLflow model lifecycles, develops feature engineering pipelines, and operates batch, streaming, and real-time inference. The role also establishes testing, observability, security, lineage, access controls, platform standards, and cost-efficient ML infrastructure.

Designs and operates production machine learning systems on Azure. Responsibilities include building MLOps workflows, productionizing Python packages, managing CI/CD and MLflow lifecycle processes, developing feature pipelines, deploying inference workloads, implementing testing and observability, and ensuring governance, security, lineage, and cost efficiency. The engineer partners with data scientists and engineers to establish scalable MLOps standards and platform architecture.

Build and maintain forecasting, driver-based models, and analytics for FP&A. Analyze historical and operational drivers, improve forecast accuracy with statistical and ML methods, create dashboards and automated reports, integrate ERP/CRM data, perform variance and scenario analysis, and present insights to finance leaders.

Lead design, build, and support of scalable data pipelines and integrations using Microsoft Fabric, ADF, PySpark, SQL, ADLS Gen2, and Databricks Unity Catalog. Drive Azure-to-Fabric migration, stabilize pipelines, tune performance, manage data governance, troubleshoot issues, document designs, mentor team members, and collaborate with stakeholders in Life Sciences regulatory environments during US Eastern hours.