Top Remote AI Engineer Jobs

Lead AI/ML Engineer to design, develop, and deploy AI-powered solutions across the full ML lifecycle. Own architecture, MLOps, monitoring, and scalability. Partner with product, data science, and platform teams, mentor junior engineers, and drive adoption of best practices for production ML and generative AI solutions.

Lead end-to-end generative AI and ML initiatives: design scalable AI architectures, build and productionize models, run experiments and A/B tests, analyze healthcare datasets, implement MLOps, research novel approaches, and mentor/hire AI/ML engineers.

Design, develop, and deploy production-grade AI/ML models and pipelines (LLMs, RAG, Transformers). Work with agentic frameworks, scale training/inference on Databricks/PySpark, integrate solutions via Docker and REST APIs, leverage cloud services (Azure/AWS), participate in AI governance, and mentor junior engineers to deliver measurable pharmacy optimization outcomes.

Design, implement, and scale AI/ML systems and data pipelines for healthcare applications. Own model inference, deployment, monitoring, and reliability; collaborate with cross-functional teams to deliver production ML solutions using cloud and distributed computing.

Design, implement, and operate production AI/ML systems for healthcare. Own inference and delivery of ML insights, scale and monitor models, build and optimize data pipelines, apply MLOps practices, and partner with cross-functional teams to integrate AI into products.

The Senior AI Engineer at RegScale designs and operates AI systems, ensuring reliability and performance while overseeing the data pipeline and compliance integration, alongside improving AI capabilities across the engineering organization.

The AI Engineer will create technical content, build sample agents, and represent Galileo at events while synthesizing developer feedback for product improvements.

Lead the design and delivery of agentic systems, integrating enterprise tools and data while ensuring security and compliance. Collaborate with clients to optimize business workflows and develop reusable components.

The AI Engineer designs, develops, and deploys AI-driven applications for non-data scientists, enhancing decision-making and model accessibility for organizations.

This role involves architecting and implementing AI solutions, focusing on agentic systems, product integration, and best practices within a team setting.

The Senior Generative AI Engineer will design and deploy AI/ML solutions, focusing on Generative AI systems and their operational excellence across various sectors like R&D and Clinical Trials.

The AI Engineer (GTM) will architect AI systems to enhance go-to-market operations, automating workflows, improving marketing and sales efficiency, and supporting customer growth strategies.

Design and build multi-agent AI systems (memory, tools, feedback), orchestrate LLMs and RAG, implement REST APIs and full-stack (Node.js/Next.js), optimize databases and cloud infra, integrate embeddings and video classification, lead design-to-deployment, enforce code quality and testing, and collaborate with product and design.

Design and build multi-agent AI systems and RAG pipelines, orchestrate LLMs and embeddings, develop scalable full-stack (Node.js/Next.js) APIs, optimize infra (SQL, AWS/GCP, Docker/K8s), lead technical design-to-deployment, ensure code quality, and implement video content classification and tagging for influencer marketing.

Design and prototype LLM- and agent-based workflows (RAG, tool-calling), build reusable connectors and agent templates, integrate data science models, define clean API boundaries, implement lightweight evaluation/logging, and collaborate with data science and leadership to operationalize AI systems.

Senior individual contributor responsible for designing and delivering production AI solutions (LLM services, RAG, document intelligence) using AWS native technologies. Build LLM-enabled APIs, vector search pipelines, and cloud-native, serverless integrations; ensure CI/CD, observability, security, and compliance. Provide technical leadership, mentor engineers, collaborate with platform and product teams, and contribute reusable AI patterns and reference implementations.

Design and develop large-scale multimodal transformer models that fuse camera, LiDAR, and radar. Research cross-modal attention and token-fusion strategies, build scalable distributed training pipelines, explore self-supervised/contrastive pretraining, and optimize models for real-time, low-latency inference.

Client-facing AI engineer who develops and deploys generative AI/LLM solutions, collaborates with data scientists and engineers, leads cross-functional projects, and delivers technical solutions and presentations to clients.

Client-facing AI Engineer who develops and deploys AI and generative AI solutions, collaborates across data science and engineering teams, leads cross-functional projects, and delivers technical solutions using analytics and BI tools to drive client outcomes.

Build production-ready intelligent automations and agentic AI solutions. Full-stack development using TypeScript or Python, LangChain, vector DBs, RAG, and tool-calling. Collaborate with clients, ensure maintainable, tested, scalable deployments, CI/CD, observability, and model fine-tuning or deployment.

The role involves researching, developing, and implementing AI solutions, optimizing models, integrating AI into workflows, and collaborating with teams to enhance business efficiency.

The Junior AI Engineer will develop intelligent agents and systems to enhance decision-making for federal missions using AI technologies.

As a Senior AI Engineer at Knotch, you will develop AI agents for workflow automation, design backend and frontend services, and collaborate with teams to integrate AI into products. Your role includes optimizing systems for performance and ensuring agent safety and governance.

Design, develop, and deploy AI/ML solutions—especially generative and agentic AI—build LLM and RAG architectures, implement MLOps pipelines, prototype PoCs, and integrate models into production using cloud, containerization, and CI/CD practices while collaborating across teams.

The role involves designing and implementing AI software solutions, collaborating with various teams, overseeing application support, and managing stakeholder communications.