Top Remote AI Engineer Jobs

Responsible for building and evolving an AI tutoring system, running experiments, collaborating with teams, and ensuring quality in model behaviors.

Build and evolve an AI tutoring system, run evaluations, design software, collaborate with teams, and ensure quality through testing and refinement.

As a Principal AI Engineer, you'll build and evolve AI tutoring systems, run experiments for learning effectiveness, design software, collaborate across teams, and prototype AI interactions.

Lead technical design and delivery of enterprise AI solutions (generative AI, RAG, agentic AI, predictive models). Own architecture, deployment, responsible AI standards, vendor evaluations, prototyping, CI/CD/LLMOps, monitoring, and mentor engineers while aligning solutions to business outcomes and regulatory best practices.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

Lead deployment and scaling of Phaidra's AI-driven control solutions for data centers and industrial facilities. Model facilities, configure and commission products, triage and support deployed systems, translate customer needs into product requirements, and act as a technical SME for customers and internal teams. Coordinate cross-functional delivery, mentor peers, and contribute to product and sales initiatives. Role may include up to 25% travel.

Design, build, and operate core systems for autonomous agents, focusing on reliability, security, and engineering effective agentic workflows and integrations for enterprise environments.

The Principal AI Systems Engineer will architect and evolve Kindo's agent platform for automating DevOps and SecOps workflows, focusing on AI-native systems engineering, reliability, and security.

You will define and build systems for autonomous agents to operate reliably in production, focusing on architectures, integrations, and security. Responsibilities include innovative system design, experimentation, and managing production-level reliability and security for enterprise-scale applications.

Embed with internal teams to identify high-leverage manual workflows and build production-grade agentic AI systems, integrations, and automation (LLM-powered interfaces, observability, APIs, dashboards) that reduce manual effort across CX, finance, ops, and logistics.

Build and ship features on an AI-native platform using Claude Code. Translate requirements into production code, review and refine AI-generated code, write automated tests, document APIs and architecture, participate in async code reviews, and identify technical debt.

Lead design and implementation of a production inference platform for LLMs. Define roadmap, integrate and optimize inference runtimes, manage GPU infrastructure, and build systems for deployment, autoscaling, observability, and operational excellence. Benchmark models/runtimes, partner with ML and product teams, make architectural tradeoffs, and mentor engineers to scale AI infrastructure.

Build and operate internal AI platform services: chat/agent frontends, retrieval over code/docs, AI-native developer workflows, sandboxed execution, model routing, telemetry, and compliance-minded design for regulated aerospace environments.

Seeking an AI Research Engineer to design AI training protocols, build scalable systems, and collaborate on novel AI infrastructure solutions.

Design, prototype, and productionize Generative AI and LLM features across backend and frontend. Integrate ML models, fine-tune, perform prompt engineering, operate inference platforms, and support AI-based product launches while participating in on-call duties.

Design and implement extraction, chunking, vector indexing, and semantic retrieval pipelines for LLMs. Optimize hybrid search, reranking, and RBAC-enabled metadata filtering. Ensure HIPAA/HITRUST-compliant handling of PHI, build evaluation frameworks for retrieval groundedness, and collaborate with architects and developers to deliver context services and hand off validated prototypes.

The AI Marketing Ops Consultant builds AI-powered marketing automation and workflows, integrates tools, documents processes, and collaborates with teams to optimize marketing operations.

Design, build, and deploy AI-powered legal applications using LLMs, RAG, and agentic AI. Partner with legal stakeholders to translate workflows, build document ingestion and vector search pipelines, ship production-grade features, implement governance/security controls, and mentor/legal users while aligning solutions with enterprise architecture and monitoring quality and evaluation metrics.

Develop, test, and maintain applications using Java, Python, or Node.js. Write clean, efficient code, debug and optimize, collaborate with cross-functional teams, use data structures/algorithms, work with SQL/NoSQL databases, participate in code reviews, and stay current on AI/ML concepts, agents, and automation tools.

Lead architecture and hands-on implementation of large-scale Generative AI and agentic systems. Build production-ready RAG, embedding fine-tuning, vector search, and MLOps pipelines on Google Cloud. Advise clients in pre-sales, mentor engineers, and ensure reliability, latency, cost, and responsible-AI controls for high-stakes deployments.

Design, train, and deploy AI features (speech-to-text, entity extraction, classification) for mobile and web products. Work with noisy, domain-specific data (shop-floor audio, repair notes, scanned documents), iterate models end-to-end from research to production, monitor performance (accuracy, latency, cost), and rapidly prototype new capabilities with product and engineering.

Lead architecture and engineering across multiple teams and principal engineers to accelerate delivery, optimize cloud costs, create reusable foundational code and POCs, mentor engineers, resolve high-priority incidents, align technology strategy with vendors, and drive portfolio-level innovation and process improvements.

Build and maintain an agentic platform that transforms high-stakes medical and legal casework into structured context. Work across the full stack: agent architectures, eval frameworks, data ingestion/indexing, and user-facing tooling. Ship workflows, design reliability-focused evals and guardrails, iterate with user feedback, and set patterns for AI-native development in a healthcare context.

Design and build internal platforms, tooling, and AI-powered workflows to reduce developer friction, improve velocity, quality, and reliability. Partner with product, infra, and security to create reusable paved paths, measure impact with engineering metrics, and help define the Developer Experience practice.

Design, build, and deploy Tier Zero self-service solutions (virtual agents, chatbots, automated troubleshooting), intelligent routing, and predictive maintenance. Integrate automation with ITSM platforms, use low-code/pro-code tools (e.g., Power Automate), track deflection and satisfaction metrics, ensure accessibility compliance, document architectures and runbooks, and coordinate with service desk and cloud teams to drive continuous improvement.