Top Remote AI Engineer Jobs

Develop and deploy practical AI solutions across internal tooling, AI platforms, and existing products. Build LLM applications using prompt and context engineering, RAG, agentic workflows, and generative AI. Create evaluation frameworks, benchmarks, testing harnesses, and production infrastructure. Apply machine learning algorithms, contribute to secure AWS-based architecture and DevOps, collaborate with product and engineering teams, and mentor junior engineers.

Designs, builds, and operationalizes secure, scalable generative AI and machine learning systems across AWS and GCP. Responsibilities include developing LLM, agentic, and RAG applications; architecting serverless, event-driven microservices; managing model experimentation, evaluation, deployment, and monitoring; and optimizing performance and cost. The role also leads responsible AI, HIPAA compliance, governance, and technical collaboration while mentoring engineers and managing team performance.

Leads enterprise AI engineering strategy, architecture, delivery, and operations. Builds and mentors AI engineering teams; oversees Generative AI, agentic AI, RAG, cloud-native platforms, and production applications across AWS and GCP. Establishes engineering standards, platform accelerators, governance, security, observability, and responsible AI practices. Partners with executives, product, security, compliance, and business stakeholders to deliver scalable, reliable, compliant AI solutions and measurable outcomes.

Build and modernize enterprise data and AI ecosystems, including real-time streaming pipelines, data products, semantic layers, knowledge graphs, RAG architectures, and GenAI infrastructure. Develop synthetic data workflows, automated data quality systems, and reliable cloud data platforms. Collaborate with architects, DevOps, and stakeholders, mentor engineers, lead prototypes, and apply AI solutions to insurance use cases while ensuring scalability, governance, monitoring, and operational resilience.

Build and deploy AI-powered solutions across Sales, Marketing, and Customer Support. Diagnose workflows, identify constraints, evaluate whether AI is appropriate, and own initiatives from discovery through deployment. Develop LLM applications using prompt engineering, RAG, agentic architectures, orchestration, guardrails, and enterprise integrations. Measure business value through flow metrics, adoption, ROI, and feedback loops while partnering with technical and non-technical stakeholders.

Build and maintain production LLM-based tools, agent workflows, reporting automation, and AI-driven analysis for enrollment and financial projections. Connect AI systems to Ellucian Colleague and other institutional systems, manage models and integrations, ensure reliability and cost control, and uphold FERPA-compliant data governance. Advise executive leadership on practical AI applications and independently turn business requests into working tools.

Design, deploy, and operate production clinical AI agents that synthesize patient data, retrieve clinical knowledge, use tools, generate care recommendations, and escalate to clinicians. Own LLM workflow reliability, observability, cost efficiency, evaluation, safety testing, RAG optimization, orchestration, and lifecycle management. Collaborate with clinical, product, and engineering teams to build reliable AI systems for chronic disease care.

Leads architecture, development, and delivery of secure cloud, data, process intelligence, and AI solutions for federal clients. Builds AWS cloud-native services, scalable data platforms, Celonis process-mining solutions, and agentic AI workflows using Amazon Bedrock and Azure OpenAI. Owns technical direction, engineering standards, security, testing, observability, deployment automation, and reusable product packaging. Supports A-123 continuous controls monitoring, agency implementations, demonstrations, and cloud environment deployments while collaborating with AWS and Celonis partners.

Provides technical leadership and architectural oversight for scalable AI-integrated software. Designs agentic AI systems, MCPs, APIs, RAG solutions, microservices, and prompt strategies; decomposes product goals into engineering tasks; reviews human- and AI-generated code; mentors distributed developers; and improves DevOps and CI/CD pipelines with AI-enhanced testing and reviews.

Build and deploy Guidewire’s production AI claims agent, including prompt and context architecture, RAG, tool integrations, human handoffs, evaluation systems, observability, and voice interactions. Improve accuracy, containment, resolution, latency, and reliability through production trace analysis and disciplined experimentation. Collaborate with insurance carriers to shape workflows, balance autonomy and risk, and harden the system for regulated claims data and real-world policyholder interactions.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

Own AI projects from discovery through production, building and deploying AI-powered applications and workflows for GTM, operations, product, strategy, and partner teams. Translate ambiguous processes into scalable systems, integrate applications with the GTM technology stack, gather user requirements, prioritize projects, and coordinate cross-functional execution. Use structured data and context to improve AI reliability and track business performance.

Leads customer-facing AI advisory engagements, identifying use cases and designing end-to-end AI deployments across model selection, training, inference, data pipelines, and evaluation. Develops proof-of-value prototypes, enables sales and partner teams, presents at conferences, publishes technical content, and advises enterprise customers on AI strategy. Collaborates across engineering, sales, marketing, alliances, and enablement while independently driving research and production initiatives.

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.

Take Deepgram's speech models and optimize, compile, and run them on resource-constrained embedded and edge hardware. Work across model compression, runtime performance (C/C++/Rust), RTOS/bare-metal and embedded Linux environments, vendor NPUs/DSPs and inference runtimes, benchmarking, deployment pipelines, OTA updates, and partner with silicon vendors to enable real-time, low-power on-device voice experiences.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Architect and build high-performance distributed infrastructure for batch and streaming data across global regions. Optimize state management, checkpointing, JVM performance, and exactly-once processing. Develop AI infrastructure including vector indexing, agentic workflows, and real-time data streaming. Own the full software development lifecycle, contribute to shared tooling and SDKs, and mentor engineers through technical leadership and design reviews.

Lead the architecture, development, optimization, and operation of production LLM and AI automation systems. Design capabilities including agents, retrieval, code generation, tool use, model routing, evaluation, and observability. Drive platform modernization, scalability, reliability, architecture, and code quality while mentoring engineers and influencing technical practices across teams. Build prototypes, establish engineering standards, conduct reviews, and communicate complex technical concepts through documentation and cross-functional collaboration.

Lead the design and development of production AI systems for enterprise application automation. Responsibilities include building reliable agents and orchestration, extracting specifications from existing systems, modeling application intelligence and retrieval, developing evaluation and CI pipelines, optimizing reliability and scale, using AI coding tools, mentoring engineers, and documenting technical practices.

Build and optimize developer-facing tools, including IDE integrations, CLIs, code-analysis systems, and CI/CD workflows. Integrate transparent, reliable AI capabilities into developer experiences, optimize high-volume production services, and improve availability, latency, and efficiency. Drive engineering best practices through code reviews, automation, documentation, and technical leadership while mentoring junior engineers. The role also requires communicating complex architectures and operating tooling across enterprise deployment environments.

Own the evaluation, selection, optimization, and production deployment of large language models powering legal and medical document workflows. Build EvalOps and monitoring frameworks, benchmark model quality, cost, speed, and resilience, manage model transitions, optimize RAG and orchestration pipelines, and reduce hallucinations. Partner with product and go-to-market stakeholders while independently implementing changes across staging and production.

Build and harden Nava’s shared AI platform for LLM-based products. Responsibilities include reusable context and agent-state management, safe long-running agent execution, sandbox integration, model routing, subagent orchestration, token-cost optimization, tracing, evaluation, testing, and documentation. The role partners with product teams to create maintainable infrastructure that supports production AI features at scale.

Lead end-to-end machine learning development for autonomous driving, including data curation, foundation-model pre-training, post-training, fine-tuning, alignment, and deployment. Build scalable pipelines using real and synthetic datasets, apply imitation and reinforcement learning, improve model safety and reliability, collaborate across teams, lead technical initiatives, and mentor ML engineers.

Lead development of scalable, reliable continuous integration infrastructure supporting autonomous-vehicle development, machine learning training, simulation, and remote builds. Specialize in Remote Build Execution and a FUSE-based file system, enabling source-code editing and developer workflows. Design and implement productivity improvements, evaluate technologies, influence technical roadmaps, establish engineering best practices, manage technical debt, and mentor engineers while balancing business and customer priorities.