Top Remote AI Engineer Jobs

Lead design, build, and deploy scalable Generative AI and ML systems into production. Implement LLM integration, prompt engineering, RAG, conversational interfaces, model monitoring, evaluation frameworks, APIs, inference optimization, and collaborate to productionize forecasting and ML models.

The role involves building and maintaining AI data capabilities, implementing data pipelines, and ensuring operational excellence in data management within a healthcare context.

Own end-to-end AI-powered product improvements: design and build cross-cutting AI services (retrieval, evaluation, guardrails), define success metrics, prototype to production, create shared AI tooling and abstractions, shape data and system architecture for safe longitudinal signals, mentor engineers, and ship reliable, secure production systems that improve educator and family outcomes.

Lead technical design and delivery of scalable, cloud-native SaaS solutions. Drive architecture and implementation across frontend and backend stacks, mentor engineers, apply AI/agent workflows and NLP, ensure high availability, and collaborate with product and cross-functional teams to ship reliable, production-grade systems.

The Senior .Net Developer will enhance SDG's C#/.NET Core codebase, design high-load services, and optimize MS SQL queries, benefiting from a remote work environment and attractive benefits.

Architect, build, and operate Redpanda’s Agentic Data Plane and cloud services across AWS, GCP, and Azure. Lead large technical projects, define architecture, develop automated provisioning and self-healing systems, and ensure reliability, scalability, security, observability, and zero-downtime upgrades. Participate in on-call operations, mitigate technical risks, and collaborate across engineering, product, and customer-facing teams.

Build and operate AI-powered automations for HVAC businesses. Map undocumented workflows, design improved AI-enabled processes, create specifications, configure workflows, monitor production runs, troubleshoot failures, add error handling and fallbacks, and identify reusable patterns across customer accounts. Lead discovery and implementation discussions with business owners and office managers while feeding field insights into the product roadmap.

Build end-to-end software features across frontend, backend, and AI components. Responsibilities include developing interfaces, APIs, backend services, data pipelines, automation workflows, and LLM-powered applications such as RAG systems and AI agents. The role involves testing, documentation, debugging, cross-functional collaboration, product iteration, and using AI coding tools to improve development speed and quality.

As an AI Applications Engineer, you will bridge AI research and implementation, driving experiments, building infrastructure, and releasing products to improve user experience and metrics.

Build AI agent discovery across Azure, endpoints, and future AWS and Google Cloud environments. Assess agent configurations, access, and security risks, then integrate findings with Delinea’s identity and attribution platform for governance. Extend existing discovery services, collaborate with platform teams, and solve complex backend engineering problems using cloud APIs and modern backend languages.

Build and scale production enterprise AI applications using RAG, LLM integrations, agents, vector search, and prompt evaluation. Develop Python/FastAPI backends, React/Next.js frontends, real-time services, and database architectures. Deploy AI workloads with Docker, Kubernetes, CI/CD, cloud infrastructure, and Terraform. Implement monitoring, testing, security, observability, and cost optimization for reliable, scalable AI systems.

Work with a business domain to map workflows, design agent-driven workflows, build and integrate agent and automation pipelines, deploy and run agents in production, monitor KPIs and observability, and iterate with human-in-the-loop checkpoints to reliably improve business outcomes.

Designs, deploys, maintains, and tunes large-scale HPC and AI clusters. Responsibilities include workload scheduling, infrastructure automation, CI/CD pipelines, monitoring, logging, alerting, storage and networking deployment, bottom-up troubleshooting, methodology documentation, and R&D support. The engineer works across hardware, operating systems, GPU computing, virtualization, cloud platforms, and software stacks to deliver performant, reliable self-service infrastructure.

Design and scale Zip’s enterprise data and AI platform across batch, streaming, structured, and unstructured workloads. Build reliable pipelines, AI-ready data products, semantic models, vector and hybrid search, and RAG capabilities. Establish standards for modeling, testing, governance, security, observability, and automation. Partner with engineering, data science, product, and business teams while mentoring engineers and influencing platform architecture.

Lead technical design and hands-on execution for complex products and systems. Design scalable architectures, write and review code, solve production issues, make technical trade-offs, mentor engineers, improve engineering practices, and drive initiatives from problem definition through production. Collaborate with product, design, and AI teams while owning system reliability, security, testing, observability, and continuous improvement in a distributed global environment.

Lead technical design and execution for complex products and engineering initiatives. Build scalable systems across backend, frontend, APIs, databases, and infrastructure while remaining hands-on with coding, reviews, debugging, and production support. Set standards for architecture, reliability, security, testing, and observability; mentor engineers; identify technical risks; improve tooling and automation; and collaborate with product, design, and AI teams to ship high-quality products.

Lead technical design and execution for complex products and engineering initiatives. Build scalable systems, write and review code, make architecture decisions, debug production issues, improve reliability, mentor engineers, and collaborate with product, design, and AI teams. The role requires end-to-end ownership from ambiguous problem definition through production, with strong product judgment and hands-on technical leadership.

Develop backend systems for 3D AutoReview, including geometry parsing, model analysis, quality detection, rule systems, scalable APIs, and AI-assisted review workflows. Collaborate with backend, frontend, product, and machine learning teams to deliver production features. Build extensible systems for custom 3D rules and reference data while experimenting with CAD, machine learning, and large language model technologies.

Designs, builds, deploys, and operates production AI/ML and LLM systems. Responsibilities include developing RAG pipelines, fine-tuning and evaluating models, managing end-to-end MLOps lifecycles, establishing monitoring and governance practices, mentoring engineers, and leading complex cross-functional client and internal initiatives.

Designs, implements, and hardens AI solutions across OneStream’s finance and analytics platform. The engineer delivers customer-facing implementations, diagnoses field issues, develops reusable demos and workflows, supports production readiness, and collaborates with Engineering, Delivery, Partners, Presales, and customers. The role requires translating technical concepts into business value for finance stakeholders, facilitating workshops, and contributing feedback to product roadmaps. Up to 25% travel may be required.

The role involves developing scalable backend systems in Go and other languages, building high-volume analytics, and designing APIs for ML workflows.

As a Senior AI Product Engineer, you will build scalable services and front-end applications, design APIs, and enhance AI product offerings, focusing on improving user experience and collaboration with engineering teams.

Senior DevOps engineer building AI-powered agents, automation pipelines, and full‑stack business applications. Design and maintain integrations (SAP, Salesforce, Google Workspace, Slack, Jira), REST APIs, CI/CD, and containerized deployments. Lead AI standards, prompt engineering, agent frameworks, cross-functional discovery, documentation, and enablement while helping grow an AI Automation & Integrations practice.

Design, build, and productionize Applied AI foundations: model access, retrieval/memory, observability, secure data paths, human-in-the-loop workflows, and reusable AI-driven platform capabilities that support product and operational teams.

As a Software Engineer in AI Infrastructure, you will design and develop core platform components, build APIs and services, enhance performance, and automate tooling while collaborating across teams and improving system reliability.