Top Artificial Intelligence Software Engineer Jobs

Build and maintain a full-stack AI-native CRM and Sales Co-Pilot: implement features, integrate ERPs, email/telephony, develop GraphQL APIs, deploy to Kubernetes, and integrate LLM-driven automation.

Own end-to-end customer implementations—discovery, integration, deployment, and optimization. Write production-grade code, convert messy workflows into automated solutions, and act as the technical face to customers to ensure successful outcomes.

Build, maintain, and scale e-commerce backend services using Python and Django. Develop REST APIs and features for catalogs, inventory, carts, checkout, orders, payments, and accounts. Integrate third-party services, optimize databases and performance, implement security controls, write tested code, resolve production issues, and collaborate on architecture, planning, reviews, deployment, and CI/CD.

Develop and optimize Salesforce Marketing Cloud Personalization solutions, including web tracking, behavioral events, audience segmentation, campaigns, sitemaps, JavaScript customizations, Einstein recommendations, and integrations with Salesforce and other systems. Troubleshoot implementations, monitor performance, conduct A/B testing, ensure data quality and privacy compliance, and collaborate with business and technical teams in a remote client-facing environment.

Develop Python applications and testing scripts for Wi-R reference hardware, including smartglasses, earbuds, wearables, EEG and EMG nodes, and health-monitoring devices. The role involves backend development, AWS server deployment, embedded programming, low-level hardware interfacing, firmware debugging, hardware compatibility validation, and test automation. Candidates should understand IoT systems, communication protocols, electrical test equipment, CI/CD, and virtualization or container technologies.

Designs, develops, tests, debugs, and troubleshoots full-stack applications using Node.js, React, and Flutter. Builds APIs, integrates databases and AI capabilities, participates in code reviews, contributes to architecture and technical direction, mentors engineers, and collaborates with cross-functional teams. The role also supports project planning, estimation, quality assurance, and adoption of modern development practices and tools.

Build and maintain enterprise applications using low-code/no-code platforms, translating customer requirements into screens, workflows, business rules, data structures, visualizations, and AI-enabled features. Configure integrations and data mappings, apply accessibility practices, perform unit, application, regression, and acceptance testing, troubleshoot production issues, and maintain documentation. Collaborate with business analysts, customers, and integration specialists in an Agile environment while supporting proposals and business growth.

Develop, test, deploy, and maintain robotic process automation and hyperautomation solutions for Department of the Navy financial management programs. Design compliant and scalable RPA architectures, integrate data and technology, research automation tools, support enterprise use cases, and maintain code and documentation. The role requires UiPath and Microsoft Power Platform expertise, collaboration with commands and clients, onsite support, domestic and international travel, and an active Secret clearance.

Lead architecture and development of Beacon’s backend Datastore platform, including PostgreSQL data models, Kafka event pipelines, GraphQL APIs, clinical-data access controls, and warehouse snapshot pipelines. Design scalable systems, tune queries, operate production databases, debug cross-system issues, write RFCs, mentor engineers, and collaborate with scientific, clinical, product, and engineering stakeholders. The role offers breadth across platform systems or depth in PostgreSQL operations and data modeling.

Build and maintain production-grade backend and full-stack software for a telecommunications product. Design APIs and reusable modules, establish CI/CD, testing, observability, and AWS deployment practices, integrate AI models and data pipelines, support testing and production operations, and mentor mid-level engineers in distributed cross-functional teams.