Top Remote AI Engineer Jobs

The AI Automation Engineer will automate internal processes, mentor non-technical staff on building tools, and ensure solutions deliver measurable business impact using AI technologies.

As a Full Stack Engineer III at Calendly, you'll develop features, scale the platform, and mentor team members while using various technologies.

The Principal Engineer will architect and develop scalable AI-driven data platforms, mentoring engineers and aligning technical strategies with business goals.

As a Senior AI Platform Engineer, you will architect AI-driven systems, develop CI/CD pipelines, and optimize infrastructure for AI workflows.

Lead the design and development of machine learning solutions, overseeing the entire software development lifecycle and mentoring engineers. Drive innovation in Generative AI and high-performance systems.

Operate and support production AI infrastructure across global datacenters, focusing on high-performance NVIDIA GPU platforms, Kubernetes, and high-speed networking. Monitor and troubleshoot infrastructure, network, hardware, and platform incidents; participate in incident response and root cause analysis; improve observability, automation, and operational runbooks.

Design, build and deploy production AI/ML solutions from complex, distributed farm data. Engineer features, select models, run experiments, move models to scalable real-time production, monitor and observe AI systems, evaluate emerging AI technologies, and collaborate with product, engineering and domain teams. Contribute to user-facing features with React/TypeScript as needed.

Design, build, and deploy production-grade AI/ML solutions that convert complex, distributed farm data into actionable intelligence. Engineer features, select models, run experiments, and move models to scalable, real-time production. Integrate, fine-tune, monitor, and observe models on cloud infrastructure (AWS, Databricks). Partner with product, engineering and domain teams and contribute to user-facing experiences when needed.

Build and maintain enterprise AI governance components (LLM gateway, Agent Firewall) across full stack (Go backend, React frontend). Ship features, harden security, integrate LLM provider APIs, collaborate with product, partners, and enterprise customers, and contribute to open-source tooling.

Lead full-stack product engineering for AI-powered healthcare features. Build frontend experiences, backend services, APIs, relational data models, and AI workflows. Prototype, ship, and iterate production systems; improve AI via evaluation and product signals. Ensure reliability, privacy, observability, and operational readiness while collaborating with product, design, customers, and engineers.

Lead ownership of the Salesforce platform with emphasis on CPQ and Billing: design and deliver Apex/LWC/Flow solutions, manage integrations (REST/SOAP, platform events, middleware), run CI/CD, enforce development best practices, mentor engineers, and apply AI-assisted tools to accelerate delivery.

Own TLDR's GTM systems end-to-end: HubSpot administration, attribution, pipeline data integrity, automations, system evaluations, and monitoring. Build dashboards and alerting, diagnose recurring issues, decide between quick-fixes or re-architecture, ship automations and agent workflows, and document operational runbooks. Partner with Applied AI to apply AI to GTM problems like attribution.

Join as a founding full-stack engineer to build an AI-native enterprise platform (agents, connectors, RAG, workflows). Own end-to-end product areas, design reusable primitives, ensure production readiness, work directly with customers, and shape roadmap and engineering practices.

Design, build, and scale AI-enabled cloud infrastructure including RAG pipelines, vector DBs, and multi-agent flows. Implement IaC (Terraform/ARM), Kubernetes (AKS/EKS), containerization, automation (Python/PowerShell), cloud security controls, and collaborate with cross-functional teams to deploy production LLM workloads.

Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.

Lead development and execution of AI-specific test plans to evaluate LLM and AI system quality, robustness, security, and compliance with OWASP AI Testing Guide and related standards. Document defects and remediation, capture evidence, validate model behavior (prompt-response, hallucinations), implement AI-driven QE approaches (autonomous test generation, self-healing automation, defect prediction), and report results to stakeholders.

Lead end-to-end development of AI-powered features and shared AI services, own product loop from discovery to production, design data and system architecture, create reusable tooling and guardrails, mentor engineers, and ensure secure handling of sensitive school and family data.

Design and build hands-on curriculum and lab environments teaching technical learners to build, configure, and deploy agentic AI on Veeva's platform. Create working AI agent demos and reference implementations, maintain advanced Java and REST API coding courses, partner with Product and Engineering, and apply instructional design to complex technical content.

Design and build hands-on technical curriculum and lab environments teaching how to build, configure, deploy, and troubleshoot AI agents. Create working agent demos, Java/REST-based exercises, partner with Product and Engineering, and apply instructional design to advanced coding-intensive courses for customers, partners, and internal teams.

Design and deliver hands-on technical curriculum that teaches customers and internal teams how to build, configure, deploy, and troubleshoot agentic AI solutions. Create working agent demos, lab environments, sample code, and instructor-led courses requiring Java and REST API fluency. Partner with Product, Engineering, and Services to shape content, apply instructional design best practices, and keep training current with agent frameworks, tool integrations, prompt design, RAG, orchestration, and evaluation.

Design, build, and deploy AI-powered internal tools and intelligent workflows to automate People, Finance, Recruiting, and operations. Identify improvement opportunities, translate business needs into technical solutions, own end-to-end execution, and collaborate across teams to ship reliable production software and AI integrations.

Design, build, and ship AI-powered internal tools and intelligent workflows to automate operations across People, Finance, Recruiting, and other teams. Own end-to-end execution from discovery to production, integrate AI services, work cross-functionally, and iterate on deployed systems.

Design and build AI-powered internal tools and automations to improve operations across People, Finance, Recruiting, and business workflows. Own end-to-end delivery from discovery to production, integrating APIs, databases, cloud infrastructure, and AI systems while collaborating with cross-functional teams.

Provide technical service and support for AI data center systems, including installation, maintenance, troubleshooting hardware and software, and collaborating with clients to ensure high-performance enterprise computing operations.

Designs, builds, and deploys production AI agents, agentic workflows, and API integrations across Microsoft and legal enterprise systems. Partners with attorneys and leadership to prioritize, govern, and scale AI solutions while enforcing security, compliance, and data governance.