Top Remote AI Engineer Jobs

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.

Lead architecture and technical strategy for an off-platform AI tier and agent stack. Design and build MCP server, internal PM and Execution agents, and an External Onboarding Agent. Implement retrieval, evals, observability, and guardrails. Drive design-partner engagements, set engineering standards, and mentor engineers to productionize LLM/agent systems.

Lead technical design and delivery of enterprise AI solutions (generative AI, RAG, agentic AI, predictive models). Own architecture, deployment, responsible AI standards, vendor evaluations, prototyping, CI/CD/LLMOps, monitoring, and mentor engineers while aligning solutions to business outcomes and regulatory best practices.

Design, build, and deploy Generative AI/LLM applications and RAG pipelines on Azure OpenAI. Develop React front-ends and Python/FastAPI backends, integrate vector databases and SQL/NoSQL data sources, support cloud infrastructure and security governance, run UAT and pilots, and produce documentation, dashboards, and automation using Power Automate and Power BI.

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Turn messy business requirements into production systems, integrate with Workday/Notion/case tools, ensure data governance and security, design multi-model LLM reliability controls, and operate full lifecycle including CI/CD, monitoring, and incident fixes while partnering with non-technical stakeholders.

As an AI Engineer, you will develop and optimize AI systems, focusing on LLM integration, vector databases, and RAG pipelines, collaborating with various teams to ensure cutting-edge performance.

Design, build, and operate customer-facing AI systems across the full product lifecycle: discovery, prototyping, deployment, evaluation, and monitoring. Translate ambiguous customer problems into AI solutions, own system design and scalability, ship production code, and partner with product teams to apply frontier models and agent frameworks.

As an AI Engineer, you'll develop and optimize LLMs, collaborating closely with cross-functional teams to enhance detection engineering capabilities and drive new AI initiatives.

The AI Engineer will design and deploy classification models for media, optimize data pipelines, lead R&D in voice and audio generation, and research image intelligence technologies.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

As a Senior AI Engineer, you will build and scale the AI Agent platform, handling customer interactions across multiple channels, while collaborating with cross-functional teams to deliver effective solutions.

The Principal AI Engineer will build and improve the core AI tutoring system, design evaluation methods, and collaborate on pedagogical goals, driving innovation in learner experiences.

Build and evolve an AI tutoring system, run evaluations, design software, collaborate with teams, and ensure quality through testing and refinement.

Responsible for building and evolving an AI tutoring system, running experiments, collaborating with teams, and ensuring quality in model behaviors.

As a Principal AI Engineer, you'll build and evolve AI tutoring systems, run experiments for learning effectiveness, design software, collaborate across teams, and prototype AI interactions.

Develop and implement AI infrastructure, focusing on distributed systems and cloud-native architecture to enable agentic workflows and high-scale data operations.

Design, build, and deploy AI-powered legal applications using LLMs, RAG, and agentic AI. Partner with legal stakeholders to translate workflows, build document ingestion and vector search pipelines, ship production-grade features, implement governance/security controls, and mentor/legal users while aligning solutions with enterprise architecture and monitoring quality and evaluation metrics.

Lead architecture and hands-on implementation of large-scale Generative AI and agentic systems. Build production-ready RAG, embedding fine-tuning, vector search, and MLOps pipelines on Google Cloud. Advise clients in pre-sales, mentor engineers, and ensure reliability, latency, cost, and responsible-AI controls for high-stakes deployments.

Develop, test, and maintain applications using Java, Python, or Node.js. Write clean, efficient code, debug and optimize, collaborate with cross-functional teams, use data structures/algorithms, work with SQL/NoSQL databases, participate in code reviews, and stay current on AI/ML concepts, agents, and automation tools.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

As an AI Engineer, you will design and optimize generative pipelines for image and video, diagnose quality issues, and implement solutions for UGC content generation. Collaborate on enhancing models and workflows while keeping up with industry trends.

Design, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.