Top AI & Machine Learning Jobs

Rapidly prototype and validate AI solutions for advisor workflows: integrate models, signals, and UX into experiments, test feasibility and performance, iterate with user feedback, document technical learnings, and hand off prototypes for productionization.

The Engineering Manager will lead AI & ML initiatives focused on data collection, overseeing technical projects and developing a high-performing team. Responsibilities include providing technical direction, mentoring staff, ensuring system reliability, and maintaining data integrity and security. The role requires expertise in NLP and data processing with an emphasis on collaborative leadership and continuous innovation.

The AI Engineer role involves prototyping AI solutions, collaborating with cross-functional teams, validating workflows, and documenting technical learnings.

Build and own analytics platforms that connect AI spend, ITSM performance, and knowledge-worker productivity. Integrate LLM telemetry with Snowflake/Fabric, create executive dashboards, identify AI enablement opportunities, normalize ServiceNow and operational data, and produce executive-ready reports measuring AI ROI and productivity impact.

The AI Engineer will design and enhance scalable AI services, collaborating with a team across the development lifecycle, while maintaining high code quality and system reliability.

Design, build, and deploy ML and generative AI solutions for marketing. Create scalable data and model pipelines, RAG/embedding workflows, agent automations, and integrate outputs into dashboards while collaborating with Marketing and Analytics.

Build and scale a unified AI/ML data collection platform: design, develop, test, deploy, and monitor LLM-driven data pipelines, RAG/embedding workflows, model lifecycle management, observability, and production MLOps systems to ensure reliable, maintainable, and cost-efficient ML capabilities.

Design, build, and operationalize a unified AI/ML data collection platform and production LLM workflows (RAG, embeddings, vector DBs). Lead model lifecycle management, observability, scalable inference, data quality/evaluation, distributed cloud-native architectures, and mentor engineers to deliver production-ready, cost-efficient ML systems.