Top Remote AI Engineer Jobs

Build and ship AI-enabled coaching product features from 0-to-1; design application capabilities on top of foundational models; translate coaching workflows into AI agents; build data pipelines and integrations; and educate the team on data and AI best practices.

The AI Engineer will build AI-powered solutions to enhance productivity and automation across departments, collaborating closely with leadership and managing integrations with existing SaaS platforms.

Lead architecture and delivery of an ML infrastructure efficiency platform across silicon co-design teams. Define platform contracts, onboarding, orchestration, auth, observability, storage/caching, and SLA enforcement. Own production operation, security, reliability, and performance while mentoring engineers and driving cross-team technical decisions.

Build and prototype state-of-the-art large language model systems and deep AI assistants, take services from ideation to production, fine-tune and compare open-source and commercial LLMs, use cloud platforms, analyze model and projection accuracy, and collaborate cross-functionally.

Act as a part-time external technical mentor for Claude/agentic-AI programs: run 1:1 calls, technical deep-dive presentations, and group Q&A; review student projects, provide guidance on Claude ecosystem, agent design, prompts, production AI systems, and best-practice engineering.

Design, build, and deploy scalable AI/ML models and pipelines for consumer healthcare. Develop production code (Python/Java/Scala), automate infrastructure (Terraform, Kubernetes), validate model performance, integrate cloud services (Azure/AWS), and collaborate cross-functionally to monitor and document enterprise-scale AI solutions.

Design, build, and evaluate AI systems, focusing on natural language understanding and automation. Collaborate with teams to ensure production quality and performance metrics.

Lead design, build, deployment, and maintenance of production-grade GenAI systems (LLM apps, agentic workflows, RAG, retrieval, and evaluation). Architect scalable ML services, implement model serving and observability, optimize latency and cost, fine-tune LLMs, translate prototypes to production, mentor engineers, and produce runbooks and documentation.

Support development and evaluation of AI features and RAG pipelines: prompt experimentation, data preparation, embedding generation, model evaluation, debugging, and basic AI service development using Python and ML tools under senior engineer guidance.

The Sr. Applied AI Engineer will design, build, and deploy production-grade ML and LLM systems. Responsibilities include collaborating with various teams, developing evaluation infrastructure, maintaining versioned LLM workflows, and ensuring data governance.

Develop and prototype GenAI software solutions in short agile sprints. Design, implement, and scale applications integrated with AI/ML models and data platforms, collaborate with data scientists, participate in agile ceremonies, and apply DevSecOps and cloud best practices while supporting CBP mission requirements.

Embed with clients to discover high-value problems and build production-ready agentic AI, data, and software solutions. Lead prototyping through deployment and operational handoff, design agent architectures, integrate APIs and data pipelines, ensure security, observability, and responsible AI practices, and drive adoption through training and change enablement.

Develop, implement, and validate production-level control and learning algorithms (MPC, RL, IL) for real-world embodied robotic systems. Design experiments to improve robustness and adaptability, integrate AI control stacks with perception and systems engineering, and use simulation and digital twins to accelerate deployment.

Design, implement, and optimize explainable clinical reasoning AI systems that integrate patient data and medical knowledge. Build RAG/embedding pipelines, backend integrations, scalable Docker/cloud-native deployments, and ensure transparent, evidence-backed model outputs while collaborating with cross-functional teams.

Design, build, and deploy AI-powered workflows, custom agents, and RAG pipelines; integrate LLM APIs into internal platforms; prototype emerging AI tools; collaborate cross-functionally; maintain code, documentation, and an AI roadmap; provide training and act as internal applied AI subject-matter expert.

Design, build, and ship agentic LLM systems and orchestration pipelines. Translate stakeholder ideas into production workflows, write and refine prompts, run evals and observability in Weave or similar, and maintain system performance and guardrails.

Build and productionize GenAI features: design experiments, evaluation suites, advanced RAG and reasoning pipelines, oversee SFT/LoRA tuning, and optimize quality-cost-latency trade-offs for reliable, user-focused AI products.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Design, build, and deploy production-grade AI/ML models, agentic workflows, and data pipelines using Python, Databricks, LangChain/LangGraph, and Azure. Collaborate with cross-functional teams to translate requirements into scalable LLM applications, implement feature engineering and validation frameworks, and embed responsible AI practices including fairness, security, and monitoring.

Design, develop, and deploy agentic AI workflows and LLM-based automations for clinical and business use. Build scalable RAG solutions, vector DB integrations, and production-grade Python services. Implement AI orchestration frameworks, ensure MLOps/LLMOps, monitor observability, and collaborate with product, clinical, security, and engineering teams to deliver secure, compliant healthcare AI capabilities.

Design, build, and scale production-grade AI applications using LLMs and RAG architectures. Develop backend services and integrations, deploy and optimize solutions on AWS/Azure/GCP, and implement containerization with Docker and Kubernetes. Collaborate across teams, contribute to architecture, code reviews, documentation, and ensure production reliability and scalability.

Design and deliver agentic systems for enterprises, integrating tools and data while ensuring safety and compliance. Collaborate with clients to optimize business workflows and system architectures.

As a Forward Deployed AI Engineer, you will develop and implement voice AI solutions for enterprise clients, ensuring system reliability and customer satisfaction while managing technical discussions and deployments.

As an Applied AI Engineer, you'll build AI agents and experiences from concept to production, collaborating across teams and optimizing systems for usability and quality.

Lead architecture, design, development, deployment, and optimization of an agentic AI platform. Partner with product, data, security, and business teams to productionize AI solutions, establish engineering best practices, mentor teams, and drive reusable AI services, automation, and operational improvements to deliver business impact.