Top Remote Senior Level Jobs in Kansas City, MO

Design, scale, and operate Runpod’s distributed storage platform across network volumes, local NVMe, and S3-compatible object storage. Tune storage and high-speed network performance, lead capacity expansions and migrations, write production automation and control-plane code, extend APIs, and build observability, dashboards, SLOs, and alerts. The role also participates in on-call operations, incident response, infrastructure-as-code practices, and long-term architecture decisions for petabyte-scale AI infrastructure.

Manage strategic client relationships and serve as the operational lead across assigned accounts. Oversee digital marketing campaign execution, project timelines, cross-functional coordination, performance reporting, client communications, and business reviews. Develop client roadmaps, recommend optimizations, identify growth opportunities, and partner with Sales to drive account expansion. The role requires strong project management, strategic thinking, communication, and stakeholder management skills, with 6+ years of relevant experience and willingness to travel as needed.

Leads the payments and banking product roadmap for a regulated cannabis fintech platform. Owns product vision, KPIs, market research, compliance requirements, partner integrations, prioritization, and customer adoption. Collaborates with executives, financial institutions, fintech providers, designers, engineers, marketing, and account teams to launch embedded payment, banking, liquidity, and credit capabilities.

Own new business across North American betting and gaming, generating pipeline, winning new logos, and managing sales processes through close. Build relationships with operators and platform providers, sell fraud prevention, AML, and identity solutions, maintain accurate CRM forecasting, and collaborate with marketing, product, partnerships, and customer success. Serve as the internal voice of the vertical while staying informed on regulatory and competitive developments.

Leads and scales the implementation team, manages and mentors implementation specialists, optimizes deployment playbooks, oversees implementation risk and quality, handles executive escalations, and partners with Sales, Product, Engineering, and Client Success. Tracks implementation KPIs, resource utilization, backlogs, and CRM data integrity while driving faster customer time-to-value, retention, and account growth.

Design and deploy production AI assistants, copilots, agents, RAG systems, and workflow automation using enterprise data. Build AI backend services, APIs, microservices, and AWS serverless workflows; integrate enterprise platforms; implement observability, responsible AI, human-in-the-loop controls, and traceability. Partner with Data, IT, and business teams to improve operational processes and decision-making while managing production incidents, model rollbacks, performance, accuracy, and cost.

Lead technical strategy and execution for multi-quarter initiatives, build and maintain software systems using AI, mentor and elevate engineers, collaborate across disciplines, and protect user privacy and security. The role involves working in an AWS and Kubernetes environment with technologies including Python, TypeScript, React, Go, Airflow, dbt, and Claude Code.

Provides advanced technical support for SaaS customers, investigating complex issues, troubleshooting integrations and APIs, managing escalations, and delivering consultative guidance. The role partners with Engineering and internal technical teams, supports Premium customers, mentors teammates, improves processes, creates help content, and manages multiple queues while meeting customer and team KPIs.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, evaluation and observability capabilities, and workflows that turn insights into action. Work hands-on with large and small models, durable orchestration, cloud deployment, inference-cost optimization, and high-volume observability data. Establish reusable engineering patterns, collaborate with product and platform teams, and mentor through strong technical contributions.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.