Top Remote AI Engineer Jobs

Design and optimize AI communication middleware for distributed multi-node, multi-GPU training. Implement C/C++ performance-critical features, improve GPU communication paths, profile and tune frameworks (PyTorch, TensorFlow, JAX, DeepSpeed), develop benchmarks, contribute upstream, and collaborate with kernel, driver, switch, and systems teams to validate and deploy high-performance networking for AI/HPC.

Lead hands-on AI development and deployment to automate Salesforce administration and improve sales effectiveness. Build, evaluate, and deploy LLM-based use cases (Gemini, Agentforce), integrate multi-source data securely, collaborate with Salesforce IT and cross-functional teams, and measure success by reduced manual administrative effort and successful AI deployments.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Lead end-to-end development and production deployment of agentic AI systems and full-stack products. Architect integrations with property management systems, improve reliability and observability, define engineering standards, and partner with product and operations to ship scalable AI agents across voice, text, and email.

Design, prototype, and deploy production-ready AI products using ML and generative AI. Lead cross-functional teams, collaborate with stakeholders to solve workflow problems, build scalable prototypes and proofs-of-concept, and apply latest ML/GenAI research techniques.

Build and operate production-grade infrastructure for real-time LLM workloads and Conversational Intelligence: reliable event-driven messaging, state/session management, observability, service boundaries, and production support including on-call and incident response. Mentor peers and harden platform interfaces for scalability and reliability.

Own and build the documentation-as-system: docs-as-code repo, CI/CD publishing pipelines, AI-augmented authoring and validation workflows, and agent-consumable content. Generate reference docs from API specs, run SME review and release notes cadence, maintain style guide and runbooks, and enable internal contributors to ship docs through automation and tooling.

Lead design and implementation of AI agent systems and document intelligence for cloud audit products. Build production-grade AI services, enable cross-portfolio AI capabilities, drive 0→1 product development, and collaborate closely with product, UX, and research teams to iterate and deliver scalable, secure AI-driven workflows.

Lead and contribute to applied AI/ML projects across the SDLC: mentor teams, design and implement LLM-based solutions (RAG, copilots), architect integrations, manage data persistence choices, CI/CD, and present solutions to clients while influencing technical direction.

Lead and mentor engineering teams in applied AI/ML projects. Define architectures, propose technologies, build reference implementations, contribute code and CI/CD pipelines, and work with clients on enterprise deployments (LLMs, MLOps, RAG, vector/graph databases). Shape engineering practices and influence business development.

The AI Developer will design and implement AI solutions for enterprise systems, integrate AI capabilities into business applications, and enhance operational efficiency through automation.

The AI Infrastructure Engineer designs, develops, and optimizes core capabilities for the AI inference platform, focusing on reliability, scalability, and operability of infrastructure and services.

As an AI Software Engineer, you will develop Generative AI solutions, manage full-cycle development, implement MLOps pipelines, and ensure compliance with AI standards.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

The Gen AI Data Engineer will design and build distributed data systems, develop data pipelines, manage data infrastructure, and integrate technologies for real-time and batch processing, contributing to scalable analytics solutions.

The Software Engineer will develop backend systems for an AI Energy Agent, including data integration, microservices, and scalable architecture, focusing on performance and reliability.

The AI-Native Data Engineer will build data pipelines, architect databases, design GCP infrastructure, and automate workflows for energy management using AI.

Design and implement AI-driven applications using Verint technology, working with cross-functional teams to enhance customer experience and operational efficiency.

The AI Developer will develop, test, and deliver NICE CXone AI solutions, focusing on chat bots and other AI systems, while also supporting production and development teams.

Lead architecture and evolution of Juniper Square’s distributed services, cloud infrastructure, and developer platform. Design scalable, multi-tenant, event-driven systems; build paved-road developer tooling, CI/CD, and observability. Hands-on coding, prototyping, and performance troubleshooting. Enable production AI/LLM workloads, ensure security/compliance, support enterprise upmarket needs, and mentor engineering teams to raise operational excellence and reliability.

The Applied AI Software Engineer will evaluate LLM-based agents, manage benchmarking, and ensure performance, safety, and reliability in healthcare settings. Responsibilities include designing evaluation plans, running experiments, collaborating with teams, and developing internal tooling for AI features.

Support CBP stakeholders to identify AI opportunities, design and refine prompts and AI-enabled workflows, prototype and validate AI outputs, train users, document use cases, and coordinate deployment and integration efforts to improve operational efficiency.

Design and build AI-enabled helpdesks and contact centers: implement agentic AI that plans and interacts, develop configurable front- and back-end systems, and solve customer problems with modern full-stack tools.

As a Principal Software Engineer, you will architect AI-enabled systems, mentor teams, and guide technical decisions for scalable products.

Lead enterprise AI workflow modernization for clinical operations: discover opportunities, design and integrate automated workflows, ensure HIPAA-compliant governance, enable adoption through stakeholder engagement, training, and operational guidance, and influence long-term automation strategy.