Top Artificial Intelligence Software Engineer Jobs

Design and build core systems for data processing and AI pipelines, ensuring reliability and scalability for enterprise clients while mentoring junior engineers.

As a Full Stack Engineer, you will develop and expand enterprise-grade products, build UX interfaces, and collaborate with teams, focusing on AI and automation.

As a Software Engineer, you'll enhance AI Engine performance, optimize app features, and collaborate on GPU/CPU optimizations with hardware partners.

The role involves designing machine learning systems, optimizing real-time data processing, and building large-scale pipelines for fraud detection.

Build and operate core AI agent infrastructure: orchestration, context management, filesystem and sandboxed execution, model routing and serving, databases, Kubernetes, networking, and cloud systems to make long-running agent workloads fast, reliable, safe, and cost-efficient.

Maintain and modernize a legacy C#/.NET codebase, design and build new cloud-based applications, write front- and back-end code, add unit/integration tests, review code, troubleshoot issues, and collaborate across teams to improve architecture and reliability.

Build and own Cogent's full‑stack AI security product experience: design, implement, and maintain backend microservices, APIs and databases; develop frontend interfaces (customer portal, knowledge engine, labeling tool, dashboards); integrate systems; ship features in a multi‑tenant/cloud environment; mentor hires and contribute to engineering, operational excellence, and hiring processes.

Senior full-stack engineers to design and build server-side (Java/Python/Node.js) and front-end (AngularJS/Polymer/Ember) applications, work with clients, and deliver in a fast-paced environment. Requires 4+ years experience, BS in CSE, and US work authorization.

Design and define ThingWorx-based IIoT solutions, develop and enhance ThingWorx widgets and applications, integrate ThingWorx with enterprise systems, mentor team members, and coordinate deployments, testing, and client communications.

Develop scalable software and analytics for clinical EHR data: data processing, statistical computing, machine learning, large-scale databases, and cloud integration. Translate research algorithms into production, gather requirements, document work, and present results.