Top Remote AI Engineer Jobs

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Build and maintain internal and customer-facing web/cloud apps and AI automations. Integrate with third-party APIs (QuickBooks, Microsoft Graph, Zoho, Zoom), implement generative-AI features, ensure security, deploy to cloud, and collaborate with leadership and IT. Work independently on design, testing, and maintenance across varied projects.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

The Software Engineer - AI Systems will design and implement systems to coordinate LLMs, build testable software stacks, conduct research, and collaborate with team members to create AI-powered security solutions.

Build and own Bloomerang's AI harness and platform services: agent workflows, tool/model integrations, orchestration, evals, and guardrails. Ship backend services (authentication/identity) primarily in C#/.NET, diagnose distributed systems issues, improve developer tooling and observability, run model evaluations, and lead responsible AI adoption to increase engineering throughput and deliver measurable product outcomes for nonprofits.

Embed with strategic higher-education partners to design, build, and ship production AI and platform integrations (LLM/RAG, LTI) across Cengage products. Own end-to-end deployments, write production code, ensure compliance and accessibility, evaluate AI safety and outcomes, mentor FDEs, and translate field learnings into product roadmap contributions.

Design, implement, and operate a private, secure AI platform and autonomous agent workforce for engineering workflows. Build agents (FastAPI/CLI/chat), integrate MCP and RAG, maintain containerized infrastructure, CI/CD validation, secrets and cost management, and enterprise integrations (GitHub, Jira, Confluence, Teams). Continuously evaluate models, improve agent reliability, and drive platform adoption across low-level systems engineering domains.

As an AI Engineer, you will build LLM systems, maintain AI solutions, collaborate across teams, and refine infrastructure integration while ensuring system uptime and accuracy.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

As an AI Engineer at Clarium, you will build and maintain data enrichment pipelines, design classification workflows, analyze datasets, and ensure data quality, primarily using Python and SQL, and work closely with senior engineers and data scientists.

As a Senior AI Engineer, you will develop backend systems and AI applications for Clarium's healthcare supply chain platform, ensuring alignment with real-world workflows and critical system components.

Lead development of a production autonomous-agent intelligence layer: implement closed-loop learning from trade outcomes, build evaluation and automated strategy generation, monitor fleet performance, manage risk and capital allocation, own model training/inference pipelines (including LLM hosting/fine-tuning), and build telemetry and data capture for real-time market adaptation.

As a Principal AI Engineer, you'll design scalable AI systems, mentor teams, develop large-scale LLMs, and optimize AI-driven cybersecurity solutions.

Lead the design and development of advanced AI agents for cybersecurity, focusing on real-time decision-making and adaptive threat responses, while mentoring teams and collaborating across functions.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

The AI Engineer will develop LLM-powered healthcare applications, improve documentation platforms, build inference pipelines, and collaborate with teams to launch features, all aimed at reducing clinician burnout.

The Senior AI Engineer will innovate AI solutions for product capabilities, enhance user experiences, build scalable infrastructure, and collaborate across teams to implement AI features effectively.

The AI Engineer will design, prototype, and deploy RAG pipelines and agentic workflows to process unstructured data for Government and Military clients. Responsibilities include collaborating on architecture and delivering technical reports while ensuring systems are scalable and reliable.

As a Gen AI Developer, you'll design and implement AI/ML software solutions, support agile delivery, and collaborate with teams to solve complex problems.

Build production-ready intelligent automations and agentic AI solutions. Full-stack development using TypeScript or Python, LangChain, vector DBs, RAG, and tool-calling. Collaborate with clients, ensure maintainable, tested, scalable deployments, CI/CD, observability, and model fine-tuning or deployment.

The role involves researching, developing, and implementing AI solutions, optimizing models, integrating AI into workflows, and collaborating with teams to enhance business efficiency.

As a Senior Applied AI Engineer, you will design, build, and maintain AI systems using LLMs and backend services, ensuring reliability and performance in production. Responsibilities include developing workflows, monitoring AI behaviors, and collaborating across teams to enhance engineering standards.

The AI Engineer will develop and deploy AI initiatives, ensuring high-quality execution and client satisfaction while collaborating with cross-functional teams.

Design and build secure, cloud-native AI applications using LLMs, RAG, and Amazon Bedrock to provide AI-assisted search, summarization, and clinical question-answering; integrate with enterprise healthcare systems using FHIR/HL7, implement vector search and prompt orchestration, ensure Fed security/privacy compliance and CI/CD, and support testing, monitoring, and governance for production readiness.

Lead technical strategy and architecture for an AI Data Engine to accelerate ML dataset generation and data warehouse operations. Hands-on staff engineer mentoring engineers, designing scalable storage, vector retrieval, LLM/agentic workflows, and collaborating with ML research and platform teams to deliver petabyte-scale data solutions.