Top Remote AI Engineer Jobs

You will define and build systems for autonomous agents to operate reliably in production, focusing on architectures, integrations, and security. Responsibilities include innovative system design, experimentation, and managing production-level reliability and security for enterprise-scale applications.

Embed with internal teams to identify high-leverage manual workflows and build production-grade agentic AI systems, integrations, and automation (LLM-powered interfaces, observability, APIs, dashboards) that reduce manual effort across CX, finance, ops, and logistics.

The AI Assisted Software Engineer will design and develop enterprise software solutions, utilizing AI-assisted tools, and architect scalable systems while collaborating across teams to improve business operations.

Build and ship features on an AI-native platform using Claude Code. Translate requirements into production code, review and refine AI-generated code, write automated tests, document APIs and architecture, participate in async code reviews, and identify technical debt.

Lead design and implementation of a production inference platform for LLMs. Define roadmap, integrate and optimize inference runtimes, manage GPU infrastructure, and build systems for deployment, autoscaling, observability, and operational excellence. Benchmark models/runtimes, partner with ML and product teams, make architectural tradeoffs, and mentor engineers to scale AI infrastructure.

Eight-week mentor-guided weekend program that provides hands-on enterprise AI workflow and automation experience. Participants build agentic systems, RAG, API-integrated workflows, and portfolio projects using tools like n8n, OpenAI, Airtable, and PostgreSQL, with deliverables such as BRDs, workflow diagrams, and prompt libraries.

Eight-week remote weekend mentorship program providing hands-on exposure to AI workflow automation, agentic workflows, n8n, API integrations, prompt engineering, RAG systems, and capstone projects focused on enterprise automation and documentation practices.

Build and operate internal AI platform services: chat/agent frontends, retrieval over code/docs, AI-native developer workflows, sandboxed execution, model routing, telemetry, and compliance-minded design for regulated aerospace environments.

Design, build, and productionize scalable AI systems for autonomous detection, RAG-backed investigations, and auto-remediation. Develop LLMs, graph-based reasoning engines, streaming pipelines, evaluation/red-team processes, and collaborate with cross-functional teams to translate attacker behavior into robust detections.

The AI/Machine Learning Engineer will develop systems for automating health benefits decisions, integrating large language models with rule-driven patterns, and ensuring compliance with regulations.

Seeking an AI Research Engineer to design AI training protocols, build scalable systems, and collaborate on novel AI infrastructure solutions.

Design, prototype, and productionize Generative AI and LLM features across backend and frontend. Integrate ML models, fine-tune, perform prompt engineering, operate inference platforms, and support AI-based product launches while participating in on-call duties.

Design and implement extraction, chunking, vector indexing, and semantic retrieval pipelines for LLMs. Optimize hybrid search, reranking, and RBAC-enabled metadata filtering. Ensure HIPAA/HITRUST-compliant handling of PHI, build evaluation frameworks for retrieval groundedness, and collaborate with architects and developers to deliver context services and hand off validated prototypes.

The AI Marketing Ops Consultant builds AI-powered marketing automation and workflows, integrates tools, documents processes, and collaborates with teams to optimize marketing operations.

Design, build, and deploy AI-powered legal applications using LLMs, RAG, and agentic AI. Partner with legal stakeholders to translate workflows, build document ingestion and vector search pipelines, ship production-grade features, implement governance/security controls, and mentor/legal users while aligning solutions with enterprise architecture and monitoring quality and evaluation metrics.

Develop, test, and maintain applications using Java, Python, or Node.js. Write clean, efficient code, debug and optimize, collaborate with cross-functional teams, use data structures/algorithms, work with SQL/NoSQL databases, participate in code reviews, and stay current on AI/ML concepts, agents, and automation tools.

Lead architecture and hands-on implementation of large-scale Generative AI and agentic systems. Build production-ready RAG, embedding fine-tuning, vector search, and MLOps pipelines on Google Cloud. Advise clients in pre-sales, mentor engineers, and ensure reliability, latency, cost, and responsible-AI controls for high-stakes deployments.

Design, train, and deploy AI features (speech-to-text, entity extraction, classification) for mobile and web products. Work with noisy, domain-specific data (shop-floor audio, repair notes, scanned documents), iterate models end-to-end from research to production, monitor performance (accuracy, latency, cost), and rapidly prototype new capabilities with product and engineering.

Lead architecture and engineering across multiple teams and principal engineers to accelerate delivery, optimize cloud costs, create reusable foundational code and POCs, mentor engineers, resolve high-priority incidents, align technology strategy with vendors, and drive portfolio-level innovation and process improvements.

Build and maintain an agentic platform that transforms high-stakes medical and legal casework into structured context. Work across the full stack: agent architectures, eval frameworks, data ingestion/indexing, and user-facing tooling. Ship workflows, design reliability-focused evals and guardrails, iterate with user feedback, and set patterns for AI-native development in a healthcare context.

Lead development of the application and cognitive layer for humanoid robots: implement behavior trees and intent-to-action orchestration, build voice applications and multi-modal fusion, integrate robotics capabilities and edge/cloud AI services, and ensure production readiness with CI/CD, telemetry, and monitoring.

Design and build internal platforms, tooling, and AI-powered workflows to reduce developer friction, improve velocity, quality, and reliability. Partner with product, infra, and security to create reusable paved paths, measure impact with engineering metrics, and help define the Developer Experience practice.

Design, build, and deploy Tier Zero self-service solutions (virtual agents, chatbots, automated troubleshooting), intelligent routing, and predictive maintenance. Integrate automation with ITSM platforms, use low-code/pro-code tools (e.g., Power Automate), track deflection and satisfaction metrics, ensure accessibility compliance, document architectures and runbooks, and coordinate with service desk and cloud teams to drive continuous improvement.

Embed with a business domain to map workflows, design agent-enabled future-state processes, build and connect production automation (LLMs, agents, RAG), run and monitor agents with observability and evals, and partner with domain leaders to deliver measurable KPI improvements.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.