Top Remote AI Engineer Jobs

Build and productionize AI agents for customer support, voice interactions, and carrier onboarding. Develop primarily backend applications in Python and Java or Go, design databases and APIs, integrate frontend components, troubleshoot production workflows, conduct code reviews, and deploy services with Kubernetes. Collaborate with product, engineering, DevOps, US teams, and customers to deliver supply chain platform capabilities.

Design, develop, deploy, and support AI solutions including generative AI applications, agents, workflow automation, document intelligence, and retrieval-augmented generation. Partner with business and IT teams to identify use cases, integrate enterprise platforms and data sources, establish prompt and evaluation standards, support AI governance and security, monitor solution performance, and train employees on responsible AI adoption.

Design, configure, and support AI agents and agentic applications within Oracle Fusion Cloud. Translate business requirements into AI-enabled solutions, develop workflow agents and automations, configure integrations, and troubleshoot security, data, performance, and production issues. Support testing, deployment, documentation, governance, and compliance while evaluating new Oracle AI capabilities. Collaborate with stakeholders and mentor team members.

Architects and develops AI-powered systems, tools, agents, backend services, APIs, and experimentation frameworks using foundational AI models. Integrates AI capabilities with microservices, AWS, Snowflake, and complex data systems while ensuring scalability, reliability, and maintainability. Translates business problems into technical solutions, prototypes AI applications, collaborates with product and engineering teams, evaluates emerging AI technologies, and documents system designs and integration patterns.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, evaluation and observability capabilities, and workflows that turn insights into action. Work hands-on with large and small models, durable orchestration, cloud deployment, inference-cost optimization, and high-volume observability data. Establish reusable engineering patterns, collaborate with product and platform teams, and mentor through strong technical contributions.

Designs, develops, tests, deploys, and supports foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Develops optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost. Partners with engineering, research, product, and program teams; contributes to technical vision and roadmaps; and mentors teams while applying emerging AI research to production systems.

Provide technical leadership and build core components for a scalable AI application platform. Design distributed systems infrastructure, improve performance and reliability, shape architecture and development practices, conduct code reviews, mentor engineers, and lead complex projects from conception through delivery. Collaborate with product, engineering, and design teams to evolve the platform and enhance developer experience.

Build and lead development of an enterprise AI platform supporting large-scale AI-assisted analysis. Responsibilities include designing, developing, testing, deploying, and supporting foundation model training, LLM inference, similarity search, guardrails, evaluation, experimentation, governance, and observability. The role also requires advancing LLM optimization for scalability, latency, throughput, hardware utilization, and cost while shaping the long-term roadmap for foundational AI systems and mentoring engineering teams.

Design, develop, test, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Partner with engineering, research, product, and program teams; contribute to technical vision and roadmap; and lead or influence teams developing responsible cloud-based AI solutions.

Architects, builds, and operates production-grade agentic AI systems for identity security at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, failure recovery, enterprise-grounded reasoning, retrieval and RAG pipelines, model integration and evaluation, safety guardrails, observability, and human-in-the-loop controls. Provides technical leadership through architecture ownership, code reviews, quality standards, and mentoring.

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.

Leads the design, development, deployment, and scaling of production AI/ML solutions for healthcare. Builds cloud-based machine learning systems, data pipelines, feature workflows, and MLOps processes including CI/CD, monitoring, drift detection, retraining, and governance. Provides architecture guidance, technical leadership, mentorship, and cross-functional partnership while applying NLP, deep learning, computer vision, foundation models, and other advanced AI techniques.

Design, build, and deploy scalable AI/ML models and pipelines for consumer healthcare. Develop production code (Python/Java/Scala), automate infrastructure (Terraform, Kubernetes), validate model performance, integrate cloud services (Azure/AWS), and collaborate cross-functionally to monitor and document enterprise-scale AI solutions.

Lead design, build, and deploy of large-scale data platforms for LLMs, RAG, and agentic AI systems. Hands-on coding, architecting fault-tolerant pipelines, establishing MLOps/DataOps best practices, mentoring engineers, and operationalizing research into production across Exabyte-scale distributed systems.

Leads the design, deployment, and operation of enterprise AI/ML, AIOps, and Digital Twin capabilities. Drives predictive analytics, intelligent automation, incident prevention, root-cause analysis, autonomous remediation, observability, and SRE practices across a mission-critical healthcare platform. Leads major incident response, mentors engineering teams, evaluates emerging technologies, and establishes scalable operational and reliability strategies.

Owns the strategy, architecture, and implementation of production AI and machine learning systems for personalization, search ranking, relevance, and patient-provider matching. Builds recommendation engines, retrieval models, LLM applications, and scalable ML infrastructure while establishing AI safety, evaluation, and MLOps standards. The role requires hands-on model development, technical leadership, mentoring, and responsible deployment of AI in a regulated mental healthcare environment.

Own end-to-end analytics engineering for AI and developer experience data. Instrument telemetry, build governed production pipelines, define trusted metrics, and create executive-facing dashboards and analyses. Partner with data engineering, AI, and data science teams to improve data quality and connect raw signals to decision-making. Present clear recommendations on AI investment, developer productivity, and ROI while using AI coding agents to build and maintain pipelines, dashboards, and analyses.

Own end-to-end analytics pipelines for AI usage, developer experience, engineering productivity, and spend. Instrument telemetry, build governed ETL workflows, define trusted metrics, and create executive dashboards and visual narratives. Analyze results, recommend improvements to AI ROI and developer experience, and communicate methodology to senior stakeholders. Collaborate across data engineering, applied AI, and data science while using AI coding agents to build, test, and maintain production data systems.

Own the architecture and long-term evolution of hybrid, multi-tenant AI compute platforms across bare-metal OpenShift and public-cloud services. Define distributed training networking, GPU utilization and cost models, GitOps governance, model-serving standards, workload placement, identity and security controls, SLOs, disaster recovery, and platform upgrade strategies. Partner with AI, privacy, and security teams to deliver HIPAA-compliant infrastructure for training and inference workloads.

Build and productionize AI agents, assistants, models, and reusable skills for Armis and its customers. Rapidly develop MVPs, collaborate with engineering to harden systems, and implement rigorous evaluations for quality, safety, and reliability. Design secure agentic workflows with guardrails and isolation, select appropriate frameworks and models, establish reusable AI development practices, consult across the organization, and document solutions for operation and evaluation.

Design, build, and maintain globally distributed, fault-tolerant backend microservices and data pipelines. Integrate and refine Large Language Models, collaborate cross-functionally, ensure high-quality code, testing, monitoring, and mentor other engineers while owning features end-to-end.

Design and deploy production-grade generative AI, NLP, LLM, RAG, and agentic AI solutions for enterprise healthcare applications. Build and fine-tune models, develop vector search pipelines, implement evaluation and safety controls, and productionize machine learning systems using cloud-native CI/CD, testing, monitoring, and observability practices. Collaborate with product, platform, data science, and healthcare teams to deliver scalable and responsible AI capabilities.

Build production-ready agentic AI applications that solve customer problems. Design workflows, plugins, and tool integrations; translate ambiguous needs into intuitive AI experiences; rapidly iterate applications into production; integrate APIs and enterprise systems; and partner with platform and product engineers. Own customer-facing quality, including user experience, reliability, performance, and evaluation, while identifying reusable patterns across deployments.

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.

Lead Affirm’s enterprise AI security review program, threat model LLM and agentic systems, review code and configurations, and develop security guardrails, tooling, policies, and monitoring. Evaluate AI-enabled SaaS vendors, identify emerging vulnerabilities, support incident response, and lead cross-functional initiatives across Security, Engineering, Legal, Privacy, and Compliance. The role requires expertise in AI security architecture, threat modeling, IAM, cloud infrastructure, policy-as-code, and regulated enterprise environments.