Top Remote AI Engineer Jobs

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.

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.

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.

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.

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.

Designs and deploys AI assistants, copilots, custom agents, and end-to-end workflow automations across business functions. Builds integrations between AI platforms and enterprise systems using APIs, middleware, and serverless architectures. Develops prompt, RAG, and agentic solutions while measuring ROI and adoption. Partners with IT Operations and Security on governance, privacy, compliance, and secure deployment. Documents solutions, trains teams, and continuously improves automations based on usage and feedback.

Design, build, and deploy agentic AI systems, orchestration pipelines, and RAG/knowledge retrieval services. Implement scalable backend services, observability, LLMOps practices, and integrations with code repositories and enterprise systems. Mentor engineers and ensure explainable, traceable AI for regulated federal healthcare environments.

The role involves engaging with AI-native companies to optimize their AI platforms using NVIDIA technologies, focusing on architecture, model performance, and collaboration with technical teams.

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

As a Senior AI Engineer, you will build and scale the AI Agent platform, handling customer interactions across multiple channels, while collaborating with cross-functional teams to deliver effective solutions.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

As an AI Engineer, you'll develop and optimize LLMs, collaborating closely with cross-functional teams to enhance detection engineering capabilities and drive new AI initiatives.