Top Remote AI Engineer Jobs

Own the architecture and technical direction of Square’s go-to-market technology stack. Design, build, and operate production AI agents and automated workflows that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations; establish AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering teams to translate business needs into scalable solutions, guide integrations and build-versus-buy decisions, and mentor engineers through influence and technical leadership.

Own the architecture and technical strategy for Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations while establishing AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering leaders to translate business needs into scalable solutions, make build-versus-buy decisions, mentor engineers, and improve business impact through data and user feedback.

Own the architecture and technical direction of Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex technical initiatives, establish reliable and secure AI evaluation practices, make build-versus-buy decisions, and partner with Sales, Product, Data, and Engineering. Remain hands-on across design, implementation, testing, deployment, and operations while mentoring engineers and influencing senior stakeholders.

Own the architecture and technical strategy for Square’s go-to-market technology stack. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex technical initiatives, establish AI reliability and security guardrails, define evaluation metrics, and guide build-versus-buy decisions. Partner with Sales, Product, Data, and Engineering leaders while mentoring engineers and remaining hands-on across design, implementation, testing, deployment, and operations.

Design, build, tune, and launch AI agents and full-stack automations for U.S. federal customers. Architect integrations with enterprise systems while meeting federal security, compliance, procurement, and ATO requirements. Advise customer stakeholders, coordinate cross-functional delivery, support product rollouts, synthesize customer feedback, create reusable solution templates, and upskill agency teams. The role requires hands-on API and LLM systems expertise, customer-facing communication, technical leadership, and up to 25% travel.

Design, train, deploy, monitor, and optimize AI/ML models powering a cloud-based voice agent platform. Responsibilities include STT, NLU, and TTS development; MLOps pipelines; AI API services; inference and latency optimization; frontend prototypes and dashboards; research, experimentation, vendor evaluation, documentation, and code quality. The role requires collaboration with backend, frontend, and infrastructure teams.

Unpaid AI engineering internship to design, build, and deploy ML solutions (NLP, computer vision, recommenders). Work on data preprocessing, model training/fine-tuning, containerized FastAPI microservices, and scalable deployments on Google Cloud Run. Contribute to code reviews, GitHub repos, and integration of third-party AI APIs.

As a Staff AI Engineer, you'll lead AI product development, overseeing technical direction, system deployment, and model evaluation while collaborating across teams.

The role involves designing AI systems for mental healthcare, focusing on building scalable, production-grade systems that enhance patient engagement and clinical operations. Responsibilities include setting technical standards and ensuring safety and reliability while working with advanced AI technologies.

Designs, develops, deploys, evaluates, secures, and maintains enterprise AI, machine learning, generative AI, and LLM solutions for government applications. Responsibilities include data preparation, model training and monitoring, evaluation, red-team testing, safety controls, documentation, governance, privacy protection, cybersecurity compliance, architecture reviews, and stakeholder collaboration. The role translates mission and technical requirements into production-ready, responsible AI capabilities.

Design, develop, and maintain enterprise AI applications, including conversational assistants, copilots, autonomous agents, RAG solutions, and workflow automation. Build scalable APIs and microservices integrating foundation models and enterprise systems. Apply prompt and context engineering, memory management, observability, governance, and performance optimization. Collaborate with cross-functional teams to identify automation opportunities and participate in Agile development, code reviews, CI/CD, and DevSecOps.

Design, build, and operationalize production AI/ML systems on Google Cloud for federal defense programs. Lead Vertex AI, MLOps, data engineering, GenAI, security, and compliance initiatives; develop scalable pipelines and deployment platforms; implement monitoring, retraining, evaluation, and secure-by-design controls. Partner with mission stakeholders, mentor engineering teams, lead technical reviews, and document architectures, controls, and operating procedures.

Leads the design, development, and deployment of AI-powered healthcare solutions using Java, Spring Boot microservices, HL7 FHIR, Da Vinci guides, Kafka, relational databases, Docker, Kubernetes, and cloud platforms. Builds REST APIs and event-driven systems across claims, eligibility, prior authorization, and provider domains. Establishes CI/CD automation, observability, security, and responsible AI practices while evaluating emerging technologies and improving operational workflows.

Integrate and fine-tune existing AI models within production software, evaluate model performance, build evaluation frameworks, configure observability, develop Python AI features, and troubleshoot real-world performance. Collaborate with data science, engineering, the CTO, and cross-functional teams to improve products and identify AI innovation opportunities. This role focuses on applying existing models rather than designing new machine learning models.

Own shared AI platform components and build production AI features, including LLM orchestration, prompt workflows, evaluation, observability, and deployment. Develop scalable SaaS backend systems, APIs, data pipelines, search, background processing, and PostgreSQL solutions. Lead CI/CD and AI tooling adoption, establish technical direction, mentor engineers, and translate ambiguous requirements into reliable systems.

Architect, build, and deploy production-grade AI/ML platforms and LLM/RAG solutions. Lead system design, CI/CD and Terraform-based IaC, ensure reliability and monitoring, mentor engineers, run incident response, and prototype emerging AI technologies.

Customer-facing engineer who designs, builds, and productionizes GenAI applications (RAG, multi-agent, Text2SQL, fine-tuning). Own production rollouts, advise customers, collaborate with product and engineering, present at conferences, and optimize/evaluate models and deployments across cloud platforms.

Design, develop, and deploy AI-native software products that transform large-scale geospatial data into actionable intelligence. Build multi-agent workflows using orchestration frameworks and large language models, engineer tool-using and self-refining agents, develop data ingestion pipelines, and operate secure, scalable AI systems in production. Apply DevOps, reinforcement feedback mechanisms, and AI-assisted development practices while contributing to engineering standards and documentation.

Designs and develops production-grade AI software for eDiscovery, including predictive and generative AI workflows, document scoring, RAG pipelines, privilege detection, APIs, and scalable Azure services. Integrates model-serving endpoints, vector search, LLM APIs, and platform connectors at multi-million-document scale. The role also requires software architecture, database development, testing, CI/CD automation, agile process improvement, and mentoring engineers.

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, RAG, model routing, tool calling, guardrails, evaluation, monitoring, and cost optimization. Ship reliable, safe, measurable AI features; develop evaluation frameworks and quality controls; monitor production behavior; and collaborate with client-facing product, engineering, platform, backend, and security teams to deliver practical AI solutions.

Lead hands-on architecture, development, deployment, and optimization of enterprise GenAI, RAG, Agentic AI, and Neo4j Knowledge Graph solutions. Build data pipelines, APIs, microservices, and cloud-native AI infrastructure; rapidly deliver customer POCs and production systems. Serve as the primary technical interface for enterprise clients, troubleshooting performance, scalability, reliability, and cost issues while providing technical consulting.

Build scalable evaluation, introspection, visualization, and metrics tools for autonomous vehicle machine learning models. Develop workflows processing billions of examples and connect offline evaluation with data, training, simulation, and launch decisions. Collaborate with modeling, data, infrastructure, and validation teams to improve model performance, safety, reliability, and development speed. Responsibilities include software design, implementation, maintenance, code reviews, and production tooling across backend, frontend, and data infrastructure.

Build and operate enterprise-scale AI solutions, including multi-agent systems, LLM architectures, RAG pipelines, distributed training, online inference, and low-latency serving. The role uses Vertex AI and Google Cloud services to create secure, compliant, observable, and production-grade AI workflows. Responsibilities include agent orchestration, governance, monitoring, automation, stakeholder collaboration, and ownership of the complete AI development lifecycle from data collection through deployment and performance monitoring.

Own the company’s AI intelligence layer end to end, including reasoning, learning loops, evaluations, guardrails, and reliability. Build and deploy applied LLM systems for real-world users, establish the AI architecture, identify and address failure modes, and eventually hire and lead the AI team. The role emphasizes production AI judgment and systems building over model research or industry experience.

Leads hands-on design, development, testing, and production delivery of generative and agentic AI solutions. Builds RAG, automation, knowledge retrieval, developer productivity, and IT operations capabilities; integrates AI with enterprise systems, APIs, data, and cloud services. Ensures security, privacy, observability, evaluation, scalability, and operational readiness while mentoring engineers, documenting reusable patterns, and coordinating technical delivery across architecture, infrastructure, security, data, and business teams.