Top Artificial Intelligence Jobs in Madison, WI

Administer and optimize Genesys Cloud CX and WFM/dialer systems for a high-volume blended contact center. Manage campaigns, forecasting, scheduling, real-time adherence, compliance, UAT, data imports, and cross-team improvements to drive KPI performance and regulatory compliance.

The role involves developing scalable backend systems in Go and other languages, building high-volume analytics, and designing APIs for ML workflows.

As an Account Manager for AI Products, you'll manage client relationships, ensure successful project execution, and collaborate with internal teams to drive client satisfaction and value realization.

The Product Marketing Manager will define and position the Atlas Platform, collaborating with product and engineering teams to translate its capabilities into customer-focused messaging, aiding go-to-market strategies and sales enablement.

Build production-grade AI-powered data tooling: extract data from Snowflake, generate and store embeddings, enable semantic search, design enrichment pipelines using LLM APIs, optimize AWS infrastructure, and create reusable services and SDKs for scalable, observable data and AI workflows.

The Enterprise Account Executive will drive revenue growth, manage relationships with insurance agencies, and oversee the sales process from prospecting to closing.

Lead and grow two teams (SRE/Infrastructure and Cloud Control Plane), own cloud platform reliability and infrastructure, drive technical direction, recruit and develop engineers, partner on roadmap, ensure operability and participate in on-call rotations.

The Senior Cloud Engineer will design cloud-native solutions, support infrastructure automation, and collaborate with teams to enhance cloud services and practices. Responsibilities include managing deployments, implementing compliance measures, and documenting cloud practices.

Lead the technical direction of a DevOps team, focusing on automation, Kubernetes optimization, and infrastructure design while mentoring engineers and ensuring project delivery.

Migrate and optimize data workloads, develop ETL/ELT pipelines, build datasets for analytics, and support BI tools while ensuring data quality and collaborating with teams.