Top Remote AI Engineer Jobs

Design, build, deploy, and maintain scalable, resilient backend services for a Voice AI platform. Lead cross-cutting projects, make architectural decisions, integrate AI platforms, implement observability and evaluation frameworks, and mentor engineers.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.

Integrate and fine-tune existing AI models into production features, build evaluation frameworks and observability for AI performance, collaborate with data science and engineering, develop AI-powered features in Python, and troubleshoot and monitor models in real-world scenarios.

Design, build, and own scalable data pipelines and evaluation systems that power production AI features and internal analytics. Ensure data quality across ingestion, modeling, and reporting, collaborate with ML and analytics teams, deploy and monitor ML systems, and establish standards for data work and evaluation.

Lead and contribute to full‑stack AI initiatives for Capital One Shopping. Design, implement, and deliver cloud-based services and AI-driven features, mentor engineers, collaborate with product teams, and evaluate emerging technologies to scale experiences for millions of users.

Build and scale AI platform infrastructure for internal agents across AWS/GCP. Integrate identity (Okta, OAuth2/OIDC), implement agentic security, containerized deployments (Docker/Kubernetes), CI/CD and Terraform-based pipelines, run vendor evaluations, and design observability/telemetry for LLM-driven workflows while collaborating with security, identity, and engineering teams.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Design, build, and deploy production ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM features, data pipelines, observability, MLOps, and containerized deployments across cloud and on-prem environments.

Lead end-to-end ML projects for automotive manufacturing: design, train, validate, deploy, and monitor models; implement MLOps/CI-CD; perform data engineering, EDA, and model evaluation; ensure data security and regulatory compliance; document and communicate results to stakeholders.

Lead design and delivery of AI-powered agentic architectures and automation across the studio pipeline. Build production-ready AI tools, prototype solutions for asset/tagging/queue management, integrate LLMs and DCC tools, define standards, monitor model drift and system reliability, produce reusable components, drive cross-team adoption, and measure automation impact while partnering with platform, security, and creative teams.

Lead design, build, and operate end-to-end AI/ML-enabled full-stack applications for clinical workflows. Develop Next.js/React frontends, Node.js backends, PostgreSQL data models, integrate LLM capabilities, deploy and monitor on Azure, implement CI/CD and security best practices, and mentor engineers.

Lead technical direction and people for an AI/ML engineering squad to design, build, and deploy production-grade ML and Generative AI (LLMs, RAG, agentic systems). Drive MLOps, CI/CD, model monitoring, Responsible AI compliance (HIPAA/PHI), architecture reviews, and mentor 5–10 engineers to deliver secure, production solutions.

Lead design and delivery of scalable Amazon Connect voice and chat solutions. Build Lambda-based integrations, integrate Lex bots and external systems, optimize routing/queues, ensure high availability and monitoring, lead production support, RCA, and continuous improvement for contact center platforms.

Lead design, development, deployment, and maintenance of AI/ML solutions (LLMs, generative models, predictive analytics) and conversational agents. Integrate AI features with web/APIs and cloud infrastructure, ensure responsible AI, security, and operational excellence, mentor team members, and partner with product and architecture to deliver scalable healthcare solutions.

Lead design, development, deployment, and maintenance of responsible AI/ML solutions (including LLMs, generative models, chatbots, and agent orchestration) for provider-facing products. Provide technical leadership, mentor engineers, align solutions with enterprise AI strategy and compliance, integrate AI into cloud architectures, and ensure production reliability and operational excellence.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Build and operate Coinbase's LLM and agent platform: design the LLM gateway, agent runtime, knowledge bases, AI spend governance, observability/eval tooling, and ship production agents while partnering cross-functionally to ensure secure, cost-effective, and responsible generative AI at scale.

Build and maintain Premonition, an internal platform for managing, deploying, and monitoring agentic LLM pipelines. Develop composable agents, tools, APIs, orchestration, and infrastructure on AWS/EKS. Optimize high-performance data layers (Iceberg/Delta/Hudi), embedded engines (DuckDB, LanceDB), and ensure observability, safety, and secure integration with SOC, IR, and threat-hunting workflows.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Design, develop, and deploy AI-native software that converts large-scale geospatial data into actionable intelligence. Build multi-agent workflows and integrate LLMs (GPT-4, Claude, Gemini) for contextual analysis and autonomous reasoning. Engineer agents with tool use, iterative self-refinement, and feedback loops; develop data ingestion and transformation layers; ensure secure, scalable cloud integrations; and deploy/operate systems in production using containerization, orchestration, and CI/CD.

Lead the creation of innovative AI solutions for healthcare systems, serve as a trusted advisor, foster collaboration, and drive operational excellence while managing complex challenges in healthcare AI.

As a Lead Software Engineer, you'll design and develop GenAI platforms, integrate LLMs, mentor engineers, and solve technical challenges for scalable solutions.

Design, implement, and deploy scalable ML training and evaluation platforms. Own end-to-end projects, make architectural decisions, drive prioritization, collaborate cross-functionally, and mentor engineers to accelerate autonomous driving model development.

Build and deploy scalable ML training and evaluation platforms, drive technical projects and architectural decisions, collaborate with partner teams, prioritize platform improvements, ensure reliability and performance, and mentor junior engineers.

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.