Top Artificial Intelligence Jobs

Build, deploy, and support production conversational AI implementations for banks and credit unions. Partner with clients and internal teams on technical strategy, integrations, troubleshooting, launches, success metrics, and platform improvements. Lead technical discussions, document best practices, support pre-sales, and mentor future forward deployed engineers. The role requires backend engineering, financial systems, telephony, API, cloud, and client-facing implementation expertise.

Implement and onboard customers to Tekion’s cloud-based automotive platform through configuration, training, workflow setup, and stakeholder coordination. Manage implementation milestones, risks, documentation, change adoption, and customer relationships. Mentor implementation specialists, gather user feedback, and communicate product insights internally. The role requires SaaS implementation experience, strong communication skills, project leadership capabilities, completion of product certification, and travel up to 90%.

Responsible for architecting and managing nEye's supply chain, including vendor engagement, materials planning, logistics, and systems implementation for a hardware company.

Own and scale paid acquisition across Google, LinkedIn, Reddit, X, and other channels. Manage full-funnel campaigns, analyze performance and conversion data, optimize budgets and return on ad spend, and run rapid experiments across targeting, messaging, creative, and channels. Collaborate with leadership and cross-functional teams on go-to-market priorities while using analytics, AI, and automation tools to improve marketing execution and growth.

Design, build, and operate resilient AWS cloud infrastructure, Kubernetes platforms, CI/CD pipelines, and infrastructure-as-code modules in a regulated medical-device environment. Improve security, scalability, performance, cost efficiency, deployments, and operational support. Collaborate with Engineering, QA, IT, and Security teams while troubleshooting distributed systems and providing technical leadership across global teams.

Develop and maintain secure C#/.NET applications and database-driven data integration solutions for a federal VA healthcare modernization project. Responsibilities include API and web development, database design, troubleshooting, testing, performance optimization, Agile collaboration, CI/CD, application security, production support, and evaluation of AI-assisted development and LLM integrations.

Owns expense-side P&L analysis, budgeting, forecasting, variance analysis, headcount and workforce cost modeling, SaaS unit economics, and executive reporting. Partners with Accounting, HR, department leaders, and go-to-market teams while maintaining financial models in Excel and Workday Adaptive Planning. Supports monthly close, reconciliations, scenario analysis, and process improvements in a remote corporate finance environment.

Own technical strategy for mid-market and enterprise sales opportunities from discovery through close. Lead architecture discussions, product demonstrations, proof-of-value exercises, security reviews, integrations, and production-readiness evaluations. Analyze traffic and logs, design deployments across web, mobile, API, CDN, cloud, and hybrid environments, address technical objections, and translate customer feedback into product input. Mentor Solutions Engineers and create enablement content.

Own an AI agent product area end to end, from customer discovery and workflow definition through launch and production outcomes. Define agent permissions, approval rules, evaluations, quality thresholds, and MSP configuration. Partner closely with design and engineering, conduct weekly research with MSP customers and technicians, support pilots, prototype prompts and workflows, and prioritize improvements using metrics such as resolution rate, escalation accuracy, time saved, and customer satisfaction.

Lead product direction and execution for an open AI infrastructure platform. Partner with founders, engineers, technical leads, customers, and researchers to connect environments, evaluations, training, deployment, and traces into cohesive workflows. Define roadmaps, prototype solutions, write product specifications, guide design and implementation, and measure adoption and user success. The role requires deep experience building technical products, preferably in developer tools, AI infrastructure, or ML platforms, with useful expertise in training, inference, multi-agent systems, reinforcement learning, continual learning, or evaluations.