Top Remote AI Engineer Jobs

The Senior AI Engineer will develop and implement advanced AI models, design agent systems, work with cross-functional teams, and ensure project delivery aligns with business needs, focusing on scalability and security of AI solutions.

The AI Engineer will design and build production systems, focusing on LLM integration, system reliability, evaluation frameworks, and high-quality product features while maintaining strong engineering practices.

This role leads data and AI engineering strategies for R&D, oversees a team of engineers, and ensures compliance with regulatory standards while fostering innovation and collaboration across functions.

The Senior Staff AI Engineer will lead the design and optimization of reinforcement learning systems for enterprise applications, focusing on scalable architectures and production deployment. Responsibilities include mentoring, collaboration, and ensuring compliance with AI ethics and safety standards.

As a Senior AI Engineer, you will architect AI systems for marketing, build generative pipelines, and ensure compliance with privacy standards, all while leading MLOps processes.

As a Full Stack AI Engineer, you'll build AI applications and products, prototype solutions, and collaborate with founders in a fast-paced environment.

As a Senior Full Stack Developer at Capnexus, you'll design and develop a cloud-native subcontractor pre-qualification platform, integrating AI and AWS services, while collaborating with cross-functional teams on deployment and testing.

The Forward Deployed AI Engineer will deploy AI solutions, work with clients on integration, and build prototypes while contributing to platform development.

No responsibilities or details were provided. The posting only lists the job title "AI Engineer" with no additional information about duties or expectations.

This role involves developing and optimizing LLM applications, architecting workflows, and integrating AI functionalities. Strong experience in AI/ML and collaboration is required.

The role involves building and scaling LLM-powered applications, managing the full product lifecycle, ensuring system reliability, and collaborating across teams to deliver impactful AI-driven features.

Design and deliver AI-enabled features for a web-based government workflow application using LLMs, RAG, and agentic patterns. Build POCs and production-ready systems, implement orchestration frameworks and prompt engineering, integrate AI/LLM APIs, maintain full-stack apps, establish observability and guardrails, and ensure security and CI/CD practices.

Own and evolve ShiftKey's AI infrastructure: production retrieval/indexing, agentic platforms on AWS, graph-based multi-hop retrieval design, platform APIs, reference agents, and cost/observability guardrails for regulated (HIPAA) workflows.

Lead design and delivery of production autonomous agents and agentic workflows: architect orchestration, integrate and optimize frontier LLMs, drive model fine-tuning and evaluation, mentor engineers, and translate business goals into safe, reliable, scalable AI-powered product experiences.

Design and build evaluation and simulation systems for generative AI agents using Dynatrace observability data. Create large-scale simulation pipelines, define metrics/datasets/judging strategies, build developer CLIs, generate adversarial scenarios, and measure agent tool-use and failure modes. Prototype, run feedback cycles, set technical strategy, and mentor engineers.

Lead development of OpenTelemetry ingest for GenAI workloads, validate integrations across AI SDKs and frameworks, align auto-instrumentation with OneAgent, drive OpenTelemetry GenAI semantic conventions, prototype and ship production features, set technical strategy, and mentor engineers.

Lead architecture and delivery of AI-native features for a multi-tenant SaaS platform. Drive cross-functional technical initiatives, design scalable microservice systems, integrate LLMs and agent frameworks, set engineering standards, and mentor engineers to elevate technical execution.

Design, build, and deploy production-grade AI/ML and LLM solutions for claims processing, eligibility, prior authorization, and payment integrity. Build scalable data pipelines, integrate models with core payer systems, ensure HIPAA/PHI-safe pipelines and Responsible AI governance, monitor and retrain models, and partner with cross-functional teams to deliver reliable, CI/CD-driven solutions.

The Senior AI Engineer will develop AI-powered features, mentor engineers, and integrate large language models to enhance user experiences and ensure product reliability.

Develop AI-powered features, collaborate with teams, integrate LLMs, ensure production quality, mentor engineers, and stay updated on AI trends.

Develop AI agents leveraging large language models, integrate external tools, and ensure production-ready AI features while collaborating across teams.

Design and supervise technical architectures for intelligent agents and LLMs, implement MCP and A2A multi-agent flows, integrate vector stores and RAG, collaborate with MLOps for continuous deployment, memory and context management, and work with product, UX, data, and backend teams to align solutions with business needs.

Architect and implement enterprise GenAI platforms (RAG and agentic systems), design knowledge retrieval, orchestration, tool-using agents and human-in-the-loop workflows, ensure production readiness and governance with product/data/security teams, define platform engineering best practices, and solve complex platform challenges with broad autonomy.

Design, build, and operate internal agentic workflows on OpenRouter to automate support or GTM operations. Integrate with ticketing/CRM systems, ensure reliability via evals, guardrails, and monitoring, measure automation impact, and iterate with support, GTM, and engineering teams.

Design, build, and operate LLM-facing backend services and evaluation systems. Develop prompts, context pipelines, tool-calling workflows, and retrieval systems. Measure and improve model grounding, consistency, latency, and cost. Ship production-ready solutions and rigorous regression/evaluation pipelines for real-world therapeutic data.