Top Remote AI Engineer Jobs

Own reliability, monitoring, and incident response for AI infrastructure; build automation and CI/CD tooling; manage Kubernetes/Docker production workloads; partner with infrastructure, security, and compliance; improve observability and documentation; develop internal full‑stack tooling in Go or Python.

Senior SRE on the IT Operations team owning reliability, monitoring, and incident response for AI infrastructure. Build automation, CI/CD and Kubernetes tooling, improve observability and documentation, and develop internal full-stack tools using Go or Python. Partner with Infrastructure, Security, and Compliance to scale secure, resilient AI deployment pipelines.

As an Applied AI Engineer at Snorkel, you'll design and implement AI solutions using state-of-the-art techniques to solve customer challenges and enhance the Snorkel platform.

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 maintain internal engineering tooling and an internal agent platform to improve developer workflows. Support adoption of agentic coding tools, implement guardrails and evaluation approaches, measure usage/reliability/cost, partner cross-functionally, produce documentation and enablement, and participate in on-call support for internal users.

Build production AI agents, LLM apps, RAG and retrieval-based systems, tool-calling and automations for fintech workflows (onboarding, KYC, risk, CRM). Ship, evaluate, and improve reliable AI-enabled systems with product and engineering teams, addressing latency, cost, security and compliance.

Design, build, test, deploy, and support internal AI-enabled web applications, APIs, dashboards, and workflow automations. Lead QA automation, integrate systems, establish engineering and deployment standards, and collaborate with stakeholders to improve operational efficiency and govern responsible AI use.

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.

Design, build, deploy, and optimize AI/ML models and pipelines (including LLMs and RAG). Integrate models into production, work with cross-functional teams, ensure monitoring and compliance, and contribute to scalable AI systems and automation.

Lead design and delivery of AI-enabled product capabilities, including agentic workflows, LLM/agent productionization, distributed inference, model lifecycle, and reusable patterns. Drive cross-functional implementation, mentor engineers, improve performance/scalability, and ensure reliable, maintainable customer-facing AI features.

As a Senior AI/ML Engineer, you will design and build end-to-end ML systems, manage ML lifecycle workflows, and ensure compliance with AI standards within the healthcare sector.

Design, build, and operate production-grade AI systems using ML, LLMs, RAG, and agentic workflows. Define evaluation strategies, implement data/retrieval/orchestration layers, apply MLOps/LLMOps, and partner with stakeholders to prioritize, monitor, troubleshoot, and improve AI solutions balancing quality, latency, cost, reliability, and maintainability.

Build production-ready agentic AI applications that integrate agents, workflows, memory, reasoning, and tools to solve customer use cases. Rapidly prototype, iterate, and ship end-to-end AI experiences; design integrations and UX, ensure reliability, performance, and evaluation; partner with platform and product teams to create reusable components and customer-ready solutions.

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.

Build and deploy high-impact AI-native products, architect agent-driven workflows, and collaborate with teams to integrate cutting-edge AI solutions into production systems.

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 ship features across multiple codebases while designing systems and workflows for safe, scalable AI agents. Implement guardrails, evaluations, and review processes for AI-generated work, propose new development lifecycles, and operate with high autonomy to advance AI-assisted software delivery.

Early-career software engineer role contributing backend/full-stack feature development, testing, CI/CD, and production releases. Collaborate on API/module design, system integration, and deployment. Support engineering of AI/LLM features (LLM APIs, basic RAG, embeddings/agents), maintain datasets/benchmarks, track performance, debug issues, and produce technical documentation.

Lead end-to-end design, implementation, deployment, and operation of production AI/ML-enabled full-stack applications. Build LLM-powered features, design scalable backend services and data models, deploy on Azure, implement CI/CD and observability, and mentor engineers while ensuring secure, compliant integration into clinical workflows.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

As a Founding AI Engineer, you will build core intelligent agent systems, prototype features, collaborate with founders, and engage with customers to enhance workflows and solutions.

The Senior AI Engineer will develop AI-driven solutions using LLMs, focusing on prompt engineering, integration, research, and data utilization for payment systems.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Design and build AI orchestration architecture using Claude Code; create reusable orchestration patterns (skills, subagents, tools, hooks); optimize for latency, error handling, observability, and testing; run experiments, document learnings, and deliver production orchestrators that accelerate platform development.