Top AI & Machine Learning Jobs

Partner with Growth, Marketing, Sales, and Product to measure and optimize marketing and growth initiatives. Build dashboards and scalable measurement frameworks, design experiments, analyze ROI and trends, provide actionable recommendations, and communicate results to technical and non-technical stakeholders.

Lead architecture of a governed, reusable data, knowledge, and skills platform on Databricks to power analytics, AI agents, and real-time decisioning. Design data, semantic, and skills layers, enforce Governance-as-Code, enable regional data residency, and build agent interfaces and APIs while partnering with Data Science, Product, and Risk teams.

Design, build, and deploy production AI agents and orchestration systems using tools like LangChain and LlamaIndex. Apply prompting, RAG, fine-tuning, and model selection. Architect agent reasoning and workflows, implement monitoring and evaluation, and integrate AI systems with backend and frontend services for scalable, reliable, compliant production deployments.

Lead end-to-end design and implementation of generative AI solutions and APIs to improve customer-facing and internal automation. Build scalable, high-concurrency backend systems, collaborate with cross-functional teams, set best practices, and mentor engineers while driving deployment and MLOps for production AI products.

Lead design and delivery of generative AI agents and architectures, build robust APIs and scalable systems, roadmap AI applications, collaborate cross-functionally, troubleshoot production issues, and drive internal efficiency and customer-facing AI solutions.

Architect and lead the Data, Knowledge, and Skills layers enabling analytics, AI agents, and real-time decision systems. Design governed Databricks-based data models, semantic layers/metric stores, agent interfaces, governance-as-code, and regional data localization to ensure compliance, quality, and consistent metrics across the business.

Partner with Product and Engineering to analyze large datasets, run experiments, and apply statistical methods to measure product performance, identify growth opportunities, set goals and forecasts, and drive product strategy through data-driven recommendations.

Build and deploy end-to-end machine learning solutions for payment and fraud risk: translate business needs into ML problems, perform data and feature engineering, train and deploy models, analyze issues, develop ML platforms/tools, and collaborate cross-functionally to deliver business impact.

Design, build, and deploy end-to-end machine learning solutions for payment risk and fraud. Responsibilities include data and feature engineering, model training, production deployment, platform/tool development, cross-functional collaboration, and ongoing analysis to mitigate fraud and deliver business impact.

Lead a zero-to-one agentic AI finance product: define strategy, design UX, iterate to product-market fit, and collaborate with engineering, design, commercial teams and customers to ship scalable AI automation across payments, payroll, bookkeeping and tax.