Top Remote AI Engineer Jobs

Lead enterprise AI platform automation across infrastructure provisioning, CI/CD, governance, observability, deployment, and lifecycle management. Build self-service capabilities for GenAI, data science, data engineering, and analytics workloads using Terraform, Kubernetes, cloud technologies, Python, and DevSecOps practices. Support agentic AI, event-driven architectures, distributed computing, and operational reliability while collaborating with engineering, architecture, governance, security, and business stakeholders. Provide technical leadership, design reviews, mentoring, and best-practice guidance.

Own end-to-end development of AI product demos, including React and TypeScript interfaces, streaming chat and agent workspaces, backend-for-frontend services, database schemas, deployment, observability, and on-call. Partner on app-builder tooling, design systems, agent-facing CLI and deployment experiences, accessibility, localization, audit trails, and enterprise readiness. Debug across backend and Kubernetes infrastructure while delivering reliable, AI-native product experiences.

Own the AI inference and runtime platform, including open-weight model serving, Kubernetes operators and autoscaling, GPU infrastructure, stateful vector and graph stores, and secure microVM-based agent sandboxing. Build Rust, Python, and Kubernetes control-plane services; manage infrastructure as code, observability, metering, lifecycle operations, upgrades, backups, and failover. Establish security controls, open-source standards, and reliable production operations across cloud and managed GPU environments.

Develop, implement, and validate production-level control and learning algorithms (MPC, RL, IL) for real-world embodied robotic systems. Design experiments to improve robustness and adaptability, integrate AI control stacks with perception and systems engineering, and use simulation and digital twins to accelerate deployment.

Lead design, build, deployment, and maintenance of production-grade GenAI systems (LLM apps, agentic workflows, RAG, retrieval, and evaluation). Architect scalable ML services, implement model serving and observability, optimize latency and cost, fine-tune LLMs, translate prototypes to production, mentor engineers, and produce runbooks and documentation.

Build and productionize GenAI features: design experiments, evaluation suites, advanced RAG and reasoning pipelines, oversee SFT/LoRA tuning, and optimize quality-cost-latency trade-offs for reliable, user-focused AI products.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Design and deliver agentic systems for enterprises, integrating tools and data while ensuring safety and compliance. Collaborate with clients to optimize business workflows and system architectures.

Design, build, and evaluate AI systems, focusing on natural language understanding and automation. Collaborate with teams to ensure production quality and performance metrics.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

Lead end-to-end development of AI-native financial products for credit union members. Use and improve AI agents, build features across a TypeScript stack, integrate with third-party APIs, monitor production, and partner with design and stakeholders to deliver reliable, scalable solutions.

Design, build, and run data-heavy features for financial products, improve data models, pipelines, migrations, and multi-tenant systems. Work in a TypeScript monorepo, operate serverless AWS infrastructure, integrate LLM APIs, monitor production, and lead pragmatic technical decisions.

Build and ship LLM- and agent-powered product experiences for North, designing interfaces, evals, observability, and feedback loops to improve reliability and user outcomes. Collaborate with modelling, product, design, and customers to turn failures and feedback into measurable product and model improvements.

You will tackle AI challenges, develop scalable software and collaborate with teams to enhance healthcare navigation using AI solutions.

Design, build, and deploy production-grade AI solutions using LLMs, AI agents, vector databases, and cloud-native architectures. Integrate AI into underwriting and enterprise systems, implement prompt engineering, monitoring, governance, and operational best practices, and support scalable, secure AI services across the business.

Lead design and delivery of production-scale generative AI systems using AWS Bedrock, RAG, vector databases, and agent frameworks. Provide technical leadership, mentor engineers, implement guardrails, evaluation pipelines, observability, and collaborate with product and engineering to deploy safe, scalable AI features.

Own evaluation, selection, and continuous optimization of LLMs powering the analytics platform. Build Eval frameworks, benchmark and migrate models, implement RAG and embedding-based pipelines, optimize cost/performance, deploy model changes to cloud production, monitor performance and drift, and document methodologies and runbooks. Collaborate with product and operations and take end-to-end ownership of AI engineering work.

Own evaluation, selection, and continuous optimization of LLMs and AI processes powering the analytics platform. Build EvalOps frameworks, benchmark and transition models, implement RAG/embedding/vector DB solutions, optimize orchestration pipelines, deploy model changes end-to-end, monitor performance/cost/drift, and document runbooks. Collaborate cross-functionally and execute production deployments.

Own evaluation, selection, and continuous optimization of LLMs and AI pipelines for legal and medical document tasks. Build Eval frameworks, implement RAG and embedding-based systems, integrate and transition models into production on cloud, monitor performance and cost, and document runbooks. Collaborate cross-functionally and take end-to-end ownership of deployments and validation.

Own evaluation, selection, and production optimization of LLMs and AI pipelines for legal and medical document tasks. Design Eval frameworks, benchmark models for accuracy, cost, and speed, implement model transitions, build and deploy RAG and embedding-based solutions on cloud, monitor production performance, and maintain documentation and runbooks with end-to-end ownership.

Own evaluation, selection, and continuous optimization of LLMs and AI pipelines. Build EvalOps frameworks, benchmark models, implement model transitions, optimize RAG/document understanding pipelines, deploy and validate production changes, monitor performance/cost, and document runbooks.

Design, build, and operate distributed GPU training infrastructure for large neural networks. Optimize multi-node/multi-GPU execution, diagnose compute/memory/network bottlenecks, improve stability and fault tolerance, and partner with research teams to productionize large-model training pipelines for reliable, high-throughput training at scale.

Build, train, evaluate, and deploy production computer vision models (detection, segmentation, OCR, barcode decoding, visual search). Own dataset quality, tooling, model releases, inference exports, and monitoring. Collaborate cross-functionally to improve model performance, robustness, and deployment for retail robotics and edge environments.

Develop and deploy machine learning solutions for construction-site understanding using computer vision, multimodal AI, VLMs, LLMs, generative AI, RAG, and agent architectures. Curate high-quality datasets, optimize models for performance across hardware targets, and build production ML pipelines in the cloud. Own components from design through deployment and monitoring while collaborating in an agile team.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.