Top Remote AI Engineer Jobs

Develop HPC and AI software workflows for experimental data processing, imaging, plasma, high-energy physics, and materials simulations. Profile applications, benchmark performance, identify bottlenecks, and advise collaborators and product teams. Build tutorial examples, translate research into software tools, and represent NVIDIA at scientific conferences and industry events.

Lead the technical direction, architecture, and delivery of production agentic AI systems. Build AI platform abstractions, agent workflows, evaluation and guardrail infrastructure, cloud-native services, and operational tooling using TypeScript/Node.js and Python. Own deployment, monitoring, troubleshooting, and measurable outcomes while partnering with product, clinical operations, and business leaders. Mentor engineers, establish reusable patterns, communicate architectural trade-offs, and ensure privacy, security, compliance, reliability, safety, and cost controls for sensitive healthcare data.

Design, build, deploy, and optimize AI/ML models and pipelines (including LLMs and RAG). Integrate models into production, work with cross-functional teams, ensure monitoring and compliance, and contribute to scalable AI systems and automation.

Develop, fine-tune, and deploy LLM-based AI solutions and automations. Integrate models with APIs, databases, CRMs and cloud platforms, implement RAG and prompt engineering, monitor production performance, and collaborate with product and engineering teams.

Design, build, fine-tune, and deploy production-grade AI and ML systems (LLMs, RAG). Develop scalable APIs, integrate AI into business systems, monitor model performance, and collaborate with cross-functional teams.

Design, build, test, deploy, and support internal AI-enabled web applications, APIs, dashboards, and workflow automations. Lead QA automation, integrate systems, establish engineering and deployment standards, and collaborate with stakeholders to improve operational efficiency and govern responsible AI use.

Partner with enterprise customers to design, build, and deploy production AI systems. Hands-on engineering: prototype, integrate, evaluate, and scale solutions while addressing reliability, latency, cost, safety, security, governance, and operational readiness. Produce reusable tooling, architectures, and guidance to accelerate deployments and drive measurable business outcomes.

Build and deploy high-impact AI-native products, architect agent-driven workflows, and collaborate with teams to integrate cutting-edge AI solutions into production systems.

The Senior AI Engineer will design and build autonomous AI agents for mental health applications, focusing on LLM integration and reinforcement learning techniques for user interactions.

Build and operate autonomous AI agents for go-to-market workflows including inbound, outbound, pipeline management, and customer engagement. Develop evaluation frameworks, observability, human oversight, and ROI measurement for production agents. Ship MCP servers, agent skills, and web applications integrating CRM and revenue systems. Set technical direction, architect shared codebases, collaborate with sellers and RevOps, and advance best practices for LLM agents and evaluations.

Design and lead development of a high-performance, multi-cloud AI platform and context layer for agentic workflows. Own the data lakehouse roadmap, context retrieval infrastructure, agent orchestration interfaces, MCP interoperability, HITL safeguards, and AI-native operational capabilities. Establish engineering standards, CI/CD, GitOps, infrastructure-as-code, observability, and cloud governance practices while guiding architecture decisions across teams and optimizing performance, security, and cost.

Build and evolve scalable generative AI software, including an agentic framework powering Atom AI and an AI asset portal. Develop production-ready web, data, and AI solutions; evaluate emerging models and frameworks; promote responsible AI practices; and ensure solutions are maintainable, cost-effective, and scalable. Partner with stakeholders, lead complex projects, demonstrate solutions, participate in strategy and ideation, and mentor other developers.

Lead end-to-end development of AI and machine learning solutions for a medical claims synthesis product. Design and deploy LLM-driven features, hybrid statistical ML and RAG systems, document extraction, and human-in-the-loop summarization workflows. Make architecture decisions, optimize production systems, evaluate model performance, collaborate with SMEs and product teams, and mentor engineers. Translate emerging AI research into reliable, scalable production applications.

Build integrations, automation, analytics pipelines, dashboards, and self-service tools for enterprise workforce AI platforms including Microsoft Copilot, Claude, and Glean. Develop adoption and usage reporting, automate platform administration and licensing governance, support AI enablement programs, and integrate privacy, security, and responsible AI practices. Lead complex projects, mentor developers, collaborate with stakeholders, demonstrate solutions, and recommend technology and process improvements.

Evaluate AI coding-agent interactions for usefulness, correctness, engineering judgment, reasoning quality, and developer trust. Provide clear, opinionated feedback, distinguish response-quality levels, and help define standards for excellent interactions with tools such as OpenAI Codex, Claude Code, and Cursor. This contract role requires staff- or principal-level software engineering experience and familiarity with modern AI-assisted development workflows.

Build and maintain backend infrastructure for AZX’s LLM gateway, including routing, rate limiting, key management, observability, sandboxing, Kubernetes services, operators, and autoscaling. Develop high-performance services in Go, Rust, or async Python using Envoy and gRPC. Support reliable model serving across cloud and customer-managed clusters, collaborate across platform teams, and help develop scalable inference and evaluation capabilities.

Develop and optimize advanced AI models, training pipelines, evaluation frameworks, and debugging systems for robotics applications. Lead rapid experimentation, model architecture development, fine-tuning, performance optimization, and interpretability efforts. Collaborate with engineering teams on training infrastructure and deployment. The role emphasizes ML planning for autonomous vehicles, diffusion and autoregressive models, reinforcement learning, and translating research into production systems.

Develop Python services, APIs, and AI-enabled production applications for enterprise use cases. Build data-centric solutions integrating enterprise systems, databases, external services, and AI techniques such as prompt orchestration, RAG, tool use, and agent workflows. Design scalable, secure architectures, implement authentication and authorization, contribute to React-based interfaces, and work across cloud and on-premises environments. The role also involves learning emerging technologies, collaborating with teams, and occasional customer-facing delivery.

Own the maintenance, security hardening, multi-tenant provisioning, deployment, and operation of an AWS-hosted AI SaaS platform. The role will also help commercialize the product through packaging, positioning, and go-to-market efforts. Applicants should have independently shipped and operated a real SaaS product and be comfortable working as the sole or near-sole engineer. Compensation is currently revenue share, equity, or another negotiated arrangement without guaranteed cash pay.

Lead design and productionalization of AI-driven features for care navigation and PBM. Rapidly prototype and ship full-stack solutions using Node.js, React, TypeScript, Ruby, GraphQL, and Postgres. Architect scalable, secure systems for sensitive healthcare data, set AI integration standards, drive technical decisions, and mentor engineers to ensure production-quality implementations.

Design, implement, and maintain AI platform components (model serving, RAG, prompt management, evaluation). Build APIs, observability, and integration patterns; collaborate with product teams, mentor junior engineers, and enforce compliance and data-handling standards for regulated financial environments.

The AI Engineer will build AI-driven workflows, integrating customer requirements into scalable solutions while managing both front-end and back-end elements in a cloud environment.

Develop and deploy multimodal foundation models and agentic AI for multi-robot coordination. Design training recipes, curate datasets, build simulation and RL environments, evaluate performance, and integrate models into real-time robotic systems with field trials.

Build and lead Replicant’s company-wide AI enablement program. Develop MCP integrations, shared agents, agent identity management, enterprise tool connections, reusable AI workflows, and cost-visibility mechanisms. Partner with Sales, Delivery, Finance, People, Engineering, and other teams to identify opportunities, implement solutions, promote AI literacy, and develop internal champions. Optimize AI systems through model routing, prompt caching, better agent design, and secure infrastructure.

Build Replicant’s internal AI enablement program by developing reusable agent workflows, MCP integrations, agent identity management, enterprise SaaS connections, and cost visibility mechanisms. Partner with Sales, Delivery, Engineering, Finance, People, and other teams to identify opportunities, implement secure AI solutions, create shared templates and skills, build AI literacy, and develop internal champions. Optimize AI infrastructure through prompt caching, model routing, tool consolidation, and improved agent design.