Top Remote AI Engineer Jobs

Build production-grade AI agents and RAG pipelines that integrate with internal tools (BookStack, Slack, Asana, Gmail). Create REST APIs, ingestion pipelines, and function-calling workflows; monitor and iterate on agent performance while collaborating with backend (.NET) and frontend (Angular) teams.

Build internal AI developer productivity tools (coding assistants, retrieval systems, workflow automation). Develop LLM-powered apps for code generation, understanding, and test automation. Integrate AI across the SDLC, embed with teams to remove friction, experiment with prompt engineering, RAG, and AI agents, and evaluate emerging AI technologies.

Lead identification, scoping, and delivery of production AI features and automation. Prototype and ship LLM-powered solutions, build RAG and agentic pipelines, create low-code workflows, integrate AI into existing systems, and partner cross-functionally to prioritize, deploy, monitor, and iterate on AI initiatives across product, operations, and business 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.

Lead discovery, rapid prototyping, and deployment of production-ready AI solutions for supply chain and manufacturing. Build Python-based RAG pipelines, semantic search, multi-agent workflows, data integrations, and enterprise platform connections. Engage frontline operations, translate business needs into technical requirements, manage agile delivery, and ensure prototypes are scalable and transferable to core engineering teams. Work across SAP, MES, shop-floor platforms, databases, telemetry, and unstructured data while collaborating with security, data, and architecture teams.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Design and implement AI/ML systems for healthcare, own inference and delivery, manage/scale production ML systems, build scalable data pipelines and APIs, apply MLOps practices (deployment, monitoring, drift detection), and work with large-scale data and distributed computing frameworks.

As a Distinguished Engineer, you will lead technical innovation in AI-driven marketing systems, mentor talent, and guide architecture design. You will collaborate across teams to optimize banking solutions and influence future technology at Capital One.

Build and scale LLM-powered analytics and retrieval systems for healthcare data. Implement RAG, text-to-SQL, retrieval pipelines, evaluation/guardrails, and compliance controls. Integrate tools/data sources, deploy on AWS, monitor model performance, and collaborate with product and senior engineers to deliver production features.

Lead the AI engineering organization and establish technical strategy for production AI systems. Drive 0→1 product initiatives, design full-stack scalable architectures, partner with customers and product leadership, translate ambiguous problems into solutions, mentor engineers, and influence roadmap while balancing speed, quality, and sustainability.

Design, build, deploy, and monitor production AI/ML systems. Work with LLMs and generative AI, create inference and evaluation pipelines, optimize performance and cost, integrate models into production applications, and collaborate with product and engineering teams to maintain reliable data and model workflows.

Lead design and deployment of production AI systems: architect model serving, inference, evaluation, and optimization pipelines; improve latency, throughput, quality, and cost; lead cross-team AI initiatives; set engineering standards; mentor engineers; partner on AI strategy and evaluate emerging models and infrastructure.

Own and improve backend services for an AI-native learning platform: build Python/Node.js services, design serverless/cloud-native architectures, implement event-driven workflows and advanced NoSQL models, integrate LLM-based features (RAG, embeddings, vector search), ensure production reliability, observability, testing, and frontend API integrations while collaborating in a cross-functional remote team.

Create end-to-end computer-use tasks for AI agents in Linux desktop environments. Design realistic multi-application scenarios, prepare files and instructions, write Python setup and automated scoring scripts, test tasks, and document them. The contract requires daily check-ins, rapid feedback, and availability throughout a three- to four-week engagement. Experience with AI tools, AI/ML projects, Linux VMs, Git, reinforcement learning environments, or computer-use benchmarks is relevant.

Lead design and implementation of production agentic AI systems that integrate LLMs with Teak's platform APIs. Build RAG pipelines, MCP/tool integrations, evaluation frameworks, and AI infrastructure on cloud (AWS/GCP/Azure). Contribute backend services in Python, ensure compliance and safety, and operate systems in production including observability and on-call duties.

The Senior Technical Data Product Manager will oversee the data product roadmap, collaborating with data engineering and architecture teams. Responsibilities include assessing legacy systems, developing product roadmaps, driving cross-functional deliveries, and ensuring technical requirements are met, aiming for swift execution and measurable impact.

As a Founding AI Engineer, you will develop an AI-powered knowledge platform, focusing on backend systems, machine learning data pipelines, and optimizing tech stack for performance while participating in the entire product lifecycle.

As an AI/ML Engineer, you will develop and optimize AI solutions for renewable energy assets, collaborating with a skilled team to deploy algorithms in real-world projects.

Lead design, build, and operation of production AI systems and inference stacks (vLLM) across AI Factory platforms. Implement RAG, MLOps, storage/networking, Kubernetes/containers, monitoring, security, and on-call incident response. Mentor engineers, engage clients, author runbooks, and contribute reusable patterns and tooling.

The AI Engineer will design, develop, and deploy AI-powered systems, improve operations with intelligent workflows, and collaborate on high-leverage AI opportunities.

Design, develop, and maintain secure MCP Servers for AI integration, ensuring scalability and system security while managing API integrations and tools for AI assistants.

As an AI Engineer, you'll lead backend implementation, build AI-powered products, and ensure high-quality systems, contributing to Elicit’s tech stack and user experience.

Responsible for designing and delivering multi-agent systems for AI products, collaborating with data scientists and offshore teams, and writing production-grade Python code.

The Full-Stack Applied AI Engineer will design and build AI-enabled products, own delivery from UI to backend, and implement robust systems with a focus on security and reliability, collaborating closely with clients and team members.

Lead frontend architecture and development for an AI-powered voice platform. Build responsive dashboards and reusable components, integrate real-time WebSocket and WebRTC voice capabilities, consume AI backend APIs, manage complex application state, and optimize performance, accessibility, and reliability. Mentor frontend developers, establish coding standards, collaborate with designers and backend teams, and develop prototypes, testing strategies, and production-ready interfaces.