Top Remote AI Engineer Jobs

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.

Lead and mentor multiple engineering teams to deliver AI-enabled MSK platform solutions. Drive AI strategy, design and deploy responsible ML solutions, ensure regulatory (CMS) compliance, oversee vendor partners, modernize platform architecture, and manage production support and incident governance.

Customer-facing technical lead owning end-to-end implementations of Moveworks AI solutions for enterprise customers. Architect integrations, design secure automations, drive adoption across multiple engagements, advise on agentic AI roadmaps, and relay product feedback while creating reusable templates and upskilling customer teams.

Customer-facing senior technical lead owning end-to-end enterprise implementations on the Moveworks platform. Architect and build LLM-based solutions and secure integrations (ServiceNow, Workday, Okta, etc.), drive adoption across multiple customers, advise on agentic AI roadmaps, translate feedback to product teams, and create reusable templates. Expect regular customer interaction and up to 25% travel.

Lead the design and development of AI/ML models for healthcare. Collaborate with business partners and other teams to ensure high-quality execution and adherence to architectural principles.

Lead the design, development, and deployment of AI and machine learning solutions, mentor teams, and drive innovations in healthcare data analysis.

As a Senior AI/Data Engineer, you will design and maintain scalable data and AI infrastructures, partnering with cross-functional teams to deliver reliable data systems and support AI-driven business capabilities.

Act as the technical presales lead for AI agent and cloud identity security across North America: scope engagements, design and run POCs and architectures across AWS/Azure/GCP, secure non-human and machine identities, create repeatable technical assets, and partner with account teams to close deals.

Own technical delivery of Moveworks AI Agents for enterprise customers: gather requirements, design and build integrations and automations, tune LLM-based solutions, advise on AI roadmaps, synthesize feedback to product, and create reusable templates to scale implementations.

Lead architecture and productionization of a multi-agent, LLM-based AI platform for identity security. Design agent orchestration, context/memory management, and scalable, low-latency infrastructure. Transition prototypes to hardened enterprise pipelines, set reliability and evaluation frameworks for non-deterministic AI, and mentor senior engineers while partnering with product and security leadership.

Lead design and delivery of agentic AI systems and LLMOps on AWS to generate, validate, and deploy infrastructure-as-code (Terraform). Build multi-agent applications, RAG pipelines, and AIOps for cloud operations; integrate AI into CI/CD and ITSM. Set technical direction, establish guardrails/policy-as-code, curate reusable Terraform modules, and mentor engineering teams.

Own end-to-end voice platform migration and scaling: LiveKit migration, real-time turn-taking, VAD, SIP/WebRTC, multi-agent handoff, training/fine-tuning voice models, inference cost optimization, and retrieval-based grounding using Supabase/pgvector.

Join as an early-career full stack engineer to build frontend and backend product features, integrate AI coding tools into development workflows, translate product requirements into implementations, and grow toward autonomous full stack delivery while improving cross-team processes.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

The Senior AI/ML Capacity Engineer will develop capacity planning models, design analytical tools, and collaborate with teams to optimize costs and efficiencies.

Lead design and deployment of scalable ML platforms and tools, manage complex technical projects, mentor junior engineers, and collaborate across teams to enhance autonomous driving systems.

As a Senior AI/ML Engineer, you'll enhance developer productivity by leading CI solutions, mentoring engineers, and implementing scalable systems.

Lead design and delivery of enterprise AI/ML and intelligent automation platforms. Define architecture strategy, build backend APIs and event-driven pipelines, deploy cloud-native solutions, drive adoption of generative and agentic AI, mentor engineering teams, and ensure security, scalability, and governance across AI initiatives.

Embed with customers to learn and rebuild critical business functions, then design and ship production GenAI/LLM systems on AWS. Own delivery end-to-end: operator immersion, design, implementation, evaluation, and stakeholder alignment while remaining hands-on in code and cloud-native ops.

As a Senior AI Engineer at Knotch, you will develop AI agents for workflow automation, design backend and frontend services, and collaborate with teams to integrate AI into products. Your role includes optimizing systems for performance and ensuring agent safety and governance.

Build and maintain evaluation, monitoring, and CI systems to ensure AI quality, reliability, and compliance. Track metrics, detect drift, improve prompting, model selection, and pipelines, productionize robust AI workflows, support audits (SOC 2), and act as a quality gate for AI-related releases.

Design, build, deploy, and optimize production AI/LLM systems and workflow platforms. Create evaluation and experimentation frameworks, translate business needs into scalable solutions, improve retrieval/prompting/agent behavior, and ensure observability, reliability, and measurable business impact while accelerating AI adoption across teams.

Architect and own core capabilities of an internal AI agent platform: design deployment patterns, shared services, observability, integrations, and developer tooling; partner with stakeholders to turn strategic goals into production-ready agent solutions; troubleshoot and unblock builders while improving platform reliability, scalability, security, and reuse.

Work onsite with portfolio companies to evaluate, integrate, and deploy third-party AI platforms and custom automations that drive measurable EBITDA impact. Lead end-to-end implementations, run instrumented proofs of concept, build custom agentic workflows and RAG systems, ensure integration/data integrity, measure ROI, and train teams for sustained adoption. Balance no-code/low-code and full-code solutions to deliver quick wins and scalable transformations.