Top Remote Senior Level Jobs in Milwaukee, WI

Lead and grow a payments engineering team responsible for payment platforms, integrations, tokenization, fraud signals, and reconciliation. Own delivery, production stability, on-call, PCI DSS compliance, vendor relationships, budget, and cross-functional coordination. Drive reliability, incident response, roadmap execution, and measurable improvements in transaction success, latency, and error budgets.

Lead design, build, and deploy Graph+GenAI solutions combining graph databases and LLMs. Act as trusted advisor to customers, architect scalable systems, write production code, integrate with orchestration and cloud platforms, optimize performance, and provide training, enablement, and product-feedback to stakeholders. Travel up to 50% to engage customers.

As a Technical Account Manager, you'll build strong customer relationships, translating business goals into technical strategies and ensuring success with Docebo's AI-powered learning platform.

Build and maintain scalable monitoring solutions and dashboards to track production, campaign, and operational data. Analyze large-scale datasets to detect anomalies, perform root-cause investigations with Engineering and Production Operations, and deliver actionable insights to improve platform reliability and operational efficiency.

Design, build, and maintain AI platform infrastructure and services. Implement scalable backend systems, REST APIs, CI/CD, observability, automated testing, and cloud-native streaming. Collaborate across teams, apply TDD, and use generative AI tools to accelerate development.

Lead Salesforce Product-to-Cash architecture, coach engineers, design HLD/LLD and roadmaps, govern integrations and billing systems, build POCs, apply AI-first solutions, and ensure scalable, secure Revenue Cloud/CPQ implementations across quoting, billing, invoicing, and revenue processes.

Own go-to-market execution and adoption for assigned products: define positioning, lead GTM readiness, enable sales and customer-facing teams, run beta and validation programs, monitor adoption and revenue, support strategic deals, synthesize customer and competitive insights, and mentor Outbound PMs to drive launch effectiveness and adoption growth.

The Senior Product Manager will oversee product development from ideation to delivery, manage product backlogs, and collaborate with various teams to understand and solve customer needs while driving product innovation in the healthcare sector.

Provide practical, production-focused mentorship to managers and executives in business-facing AI programs: guide project work, clarify generative and agentic AI concepts, discuss deployment trade-offs, and share strategies for evaluation, governance, and scaling AI in organizations.

Provide part-time technical mentorship to learners in the Data Product Manager track: answer forum questions, host weekly live sessions, escalate issues, and guide students on data foundations, product KPIs, experiment design, data pipelines, modeling, visualization, and product frameworks.

Own and deliver advisor-focused product workflows (portfolio analytics, performance, options, bonds). Lead discovery with users and data, write PRDs, partner with engineering and design, balance correctness and usability, and be accountable for outcomes from problem definition through launch.

Design, build, and maintain end-to-end, accessible, secure full-stack software for a federal health/civilian agency. Build Section 508-compliant UIs, backend APIs, and services; follow API-first, CI/CD, automated testing, and security best practices; identify and remediate technical debt; mentor engineers and collaborate with cross-functional teams to deliver reliable, performant government software.

Design, develop, and maintain scalable ML and AI platform infrastructure for model training, deployment, feature engineering, candidate generation, agent deployment, and observability. Collaborate with data scientists and engineers, support platform operations, drive improvements, codify best practices, and influence platform investments to enable production ML systems at scale.