Top AI & Machine Learning Jobs

Build and maintain scalable data, feature engineering, inference, and machine learning pipelines. Deploy and manage end-to-end ML systems using MLOps tools, implement CI/CD and workflow orchestration, monitor model and data quality, troubleshoot production issues, and develop reusable platform components. Collaborate with data scientists, product, and business teams to deliver reliable AI/ML forecasting solutions across cloud and on-premises environments.

Senior Product Manager responsible for the vision, strategy, roadmap, backlog, and delivery of complex AI and agentic products. The role defines AI-powered workflows, prioritizes features, establishes product metrics, leads agile ceremonies, partners with engineering and business teams, and guides solutions from discovery through production. Responsibilities include designing agentic experiences, evaluating quality and business impact, managing cross-functional stakeholders, and ensuring enterprise readiness across security, privacy, compliance, and governance.

Leads the architecture, development, and operation of a scalable AI/ML platform supporting deep learning, LLMs, agentic AI, MLOps, and LLMOps. Optimizes compute and storage resources, builds reusable APIs and frameworks, manages data pipelines, and establishes deployment, monitoring, evaluation, governance, security, and responsible AI standards. Provides technical leadership, mentors engineering and data science teams, and drives cross-functional platform improvements.

Leads engineering teams within Lowe’s Innovation Labs to explore, prototype, pilot, and scale emerging technologies, including applied AI, autonomous agents, robotics, computer vision, and spatial computing. Establishes engineering standards, evaluates architectures, guides R&D portfolios, develops reusable platforms, and coaches multidisciplinary teams. Partners with executives and cross-functional stakeholders to connect technology strategy with customer and business needs, while communicating roadmaps, risks, findings, and recommendations.

Supports data science initiatives by mining and analyzing data, applying statistical, machine learning, deep learning, and generative AI techniques, and developing scalable business solutions. The role collaborates with senior scientists and cross-functional partners, contributes to predictive and prescriptive modeling, communicates findings, measures business impact, and participates in a structured Launchpad program with bootcamp training and technology rotations.

Build and deploy predictive, forecasting, pricing, promotion, and machine learning models using large-scale retail data. Apply statistical modeling, causal inference, and experimentation to support demand forecasting, inventory planning, merchandising, and supply chain decisions. Partner with Product, Engineering, Analytics, and Business teams to define requirements, evaluate model performance, and translate complex findings into actionable recommendations and production-ready solutions.