Top Remote Senior Level Artificial Intelligence Software Engineer Jobs

Build and optimize BizzyCar’s full-stack platform using Ruby on Rails APIs and Angular. Own features end to end, diagnose issues across UI, APIs, and data, guide architecture, mentor engineers, improve testing standards, and support multiple product domains including recalls, AI, and service systems.

Build and scale full-stack applications using Ruby on Rails, React, PostgreSQL, Docker, Kubernetes, and Terraform. Design APIs, data architectures, synchronization systems, infrastructure, and CI/CD pipelines for secure, reliable operation in disconnected and low-bandwidth environments. Collaborate across engineering, business development, and mission teams while supporting critical defense and public safety operations.

Build and scale reliable software systems supporting donor platforms, cash payments, and rapid-response programs. Own features end to end from architecture through deployment and monitoring, improve CI/CD and testing, troubleshoot production issues, and collaborate with product, operations, and data teams. The role also contributes to technical roadmaps and sustainable engineering practices in a mission-driven, fully remote organization.

Develop and maintain complex full-stack web applications using React and Node.js. Lead software through the full SDLC, from requirements and system design through deployment and maintenance. Build accessible, performant client-side applications, micro-frontends, and services; integrate frontend and backend components; and ensure quality through continuous integration, code reviews, and automated testing. Collaborate with Product, UX, engineering teams, and healthcare-domain professionals using Agile delivery practices.

Own the product surface and underlying services for an AI-powered workflow platform. Build review, correction, and approval interfaces; durable state, jobs, queues, retries, and core domain models; and deliver features end to end from schema through user interface. The role requires strong Python skills, comfort with probabilistic systems, product judgment, customer engagement, and experience building at an early-stage company.

Build and own large-scale backend and full-stack systems supporting AI agents, data integrations, sync engines, and construction financial products. Design major subsystems, make architecture and build-versus-buy decisions, debug complex production issues, improve scalability and performance, and mentor engineers. Work across cloud infrastructure, distributed systems, legacy on-premise integrations, caching, data consistency, and AI-powered workflows while collaborating directly with founders and customers.

Design and scale highly available backend services for an AI-powered manufacturing platform. Build AWS-based event-driven systems with Node.js, REST/GraphQL APIs, and database integrations. Use AI coding agents while validating correctness, write tests and documentation, improve observability and reliability, debug production issues, and collaborate with product, frontend, and infrastructure teams. Contribute to technical design, code reviews, engineering best practices, and responsible adoption of AI-assisted development.

Develop scalable backend services and APIs, create coding tasks and deterministic verifiers for AI systems, debug complex codebases, optimize performance, refactor legacy systems, implement features, and provide technical reviews and reasoning. The role is remote, contract-based, and part-time at 15–20 hours per week.

Lead the architecture, development, and deployment of scalable APIs and backend systems for Afresh’s AI-powered grocery ordering platform. Partner with product, account management, design, and engineering teams to deliver integrated solutions used across thousands of stores. Drive projects from conception through launch, make high-impact architectural decisions, understand complex business dependencies, and mentor junior engineers.

Design and maintain scalable services, APIs, distributed systems, and data pipelines for AI training data infrastructure. Own systems through design, implementation, testing, deployment, and monitoring. Lead architectural decisions, improve reliability and scalability, mentor engineers, and collaborate with AI/ML, product, and data operations teams. Contribute to CI/CD, containerization, DevOps, and cloud deployment practices.