Top Remote AI Engineer Jobs

Design, train, and deploy AI/ML models and scalable applications across the company. Work with structured and unstructured data, implement MLOps and containerized production deployments, monitor model performance, and ensure ethical, explainable AI. Collaborate with data scientists, engineers, and business stakeholders to translate requirements into AI-driven solutions and produce technical documentation.

Build LLM-based applications and agentic coding solutions to automate healthcare workflows. Work with non-technical stakeholders to design, deploy, monitor, and iterate production AI systems while ensuring security, privacy, and cost management.

The AI Developer will design, implement, and deploy AI agents and workflows, integrating advanced models and ensuring reliable performance in production environments.

Design, build, and deploy production-ready LLM APIs and GenAI solutions on AWS. Implement RAG pipelines, data lake pipelines, serverless workflows, prompt orchestration, and AI dashboards. Collaborate with product, DevOps, and architecture teams, document solutions, and support client handoffs while staying current with AWS GenAI and MLOps innovations.

Lead technical direction and architecture for DAP Repository Flows, design and ship autonomous and scheduled agents that operate on customer repositories, improve DAP onboarding, collaborate across teams on platform interfaces, mentor engineers, and solve high-scale technical problems to raise engineering quality.

Design, build, and deploy production-grade ML and AI solutions for behavioral health. Architect end-to-end MLOps pipelines, optimize and monitor models in production, evaluate emerging AI trends, collaborate cross-functionally, and provide technical leadership and mentorship to engineering teams while ensuring responsible, scalable, and secure AI deployments.

Lead cloud migrations for voice contact center solutions, design AI/ML capabilities, improve automation and governance, and ensure production readiness while collaborating with cross-functional teams.

Lead technical design and delivery of production AI/ML and GenAI solutions (RAG, LLMs, agents). Build, tune, deploy, and monitor models and APIs, own MLOps practices, operate cloud AI workloads, ensure software quality and responsible AI, and partner with cross-functional stakeholders while mentoring engineers.

Build and own Eve's marketing site and GTM engineering stack: integrate and optimize external tools, design AI agents for sales and marketing, implement webhooks and middleware to sync product/CRM data, and create programmatic campaigns and internal tools in partnership with Marketing, Sales, RevOps, and Product to drive growth and automation.

Own and improve the AI Social render pipeline and agent loops that generate on-brand Instagram carousels and reels. Build prompts, structured outputs, tool-calling, and media composition to ship high-quality generative image, video, voice, and music products in collaboration with product, design, and content strategy.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.

Designs and deploys lightweight AI applications and user interfaces using low-code platforms, Python, SQL, and cloud data technologies. Translates generative AI, agentic AI, and NLP capabilities into scalable business workflows, collaborating with data engineering, data science, IT, and architecture teams. Responsibilities include application testing, troubleshooting, optimization, accessibility, compliance, production deployment, incident support, and Agile development. The role requires five years of application development experience and two years deploying cloud-based applications and modern data platforms.

Develop and productionize AI agents and features for Cisco’s Circuit platform. Conduct applied research on model routing, batch capacity, fine-tuning, token optimization, and real-time workload management. Evaluate and integrate new AI models, improve cost and performance, and apply agentic AI to business automation. Lead feature delivery from concept through production, assess AI-generated code quality and security, drive cross-functional alignment, and mentor engineers.

Build and deploy AI agents that automate internal business processes. Responsibilities include authoring reusable agent skills, connecting agents to systems of record through MCP, creating evaluation sets, deploying production workflows with permissions and audit trails, supporting agents in production, and partnering with data engineers. The role requires strong Python, SQL, LLM application development, agent tooling, coding-agent experience, operational deployment skills, and the ability to translate business processes into reliable automated workflows.

Leads enterprise quality engineering and test automation strategy across UI, API, integration, performance, and data layers. Architects Java-based automation frameworks, integrates quality gates into CI/CD pipelines, and drives AI/LLM adoption for test generation, debugging, self-healing, test data creation, and defect analysis. Establishes ETL and data validation practices, governs DevOps quality standards, mentors QA teams, and collaborates with Engineering, DevOps, Data Engineering, and Product stakeholders to improve quality maturity and delivery predictability.

Design, build, and operate a secure, scalable enterprise Generative AI platform on AWS (including AWS Bedrock). Own platform lifecycle: architecture, deployment, operations, monitoring, incident management, reliability, and governance. Implement LLM inference, RAG, Agents, guardrails, observability, security controls, access management, compliance, and cost optimization. Support production-grade GenAI applications in regulated/financial environments.

Designs, provisions, and operates secure, scalable cloud environments supporting AI, ML, LLM, and data engineering solutions. Builds Infrastructure as Code, DevOps pipelines, containerized and serverless deployments, observability platforms, and compliance guardrails. Reviews infrastructure designs, creates operational documentation, and leads cloud infrastructure decisions on client engagements. Collaborates with architects, engineers, client IT, and security teams across development and production environments.

Lead quality engineering for a backend-heavy Immigration AI platform. Design test plans and cases, establish test environments, execute manual and automated testing, build custom frameworks and tooling, and test REST, asynchronous, event-driven, and backend services. Mentor engineers, conduct code reviews, identify automation opportunities, apply GenAI and LLMs, perform load and stress testing, and improve testing pipeline scalability, performance, and efficiency.

Lead AI Engineer defines AI strategy, architecture, and standards; makes build vs. buy decisions; architects scalable, reliable LLM/GenAI systems; establishes LLMOps, evaluation, guardrails, and observability; coordinates cross-functional alignment with product and engineering; and champions AI-assisted developer tooling and responsible AI practices.

Build and refine AI-powered prototypes for supply chain and manufacturing operations. Engage with factory and warehouse teams, translate operational needs into software requirements, develop Python-based RAG and agentic systems, integrate APIs and data sources, and iterate prototypes based on frontline feedback. The role involves SQL and document data preparation, agile collaboration, occasional manufacturing-site travel, and progressive adoption of enterprise AI frameworks and production engineering practices.

Build intuitive, high-performance React interfaces for AI-powered insurance workflows. Collaborate with design and backend teams, create reusable UI components and design systems, integrate frontend features with AI services (document review, entity extraction, quote comparison), rapidly prototype, and iterate based on user feedback.

Work directly with insurance customers to deploy and configure AI-powered workflows, integrate with agency management systems and messy customer data, debug integrations, build tooling, and relay product requirements to the core platform while partnering with sales and customer success.

Build and productionize AI features across frontend and backend: document review, entity extraction, quote comparison, embeddings and LLM integrations, prototype from customer feedback, and create scalable reusable systems for insurance workflows.

Design, implement, and ensure quality of systems stack, tools, and SDK integration for AI frameworks. Collaborate with hardware and ML teams, apply engineering principles, write low-level/high-performance code, define architecture, estimate scope, and drive coding standards.

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.