Top Remote AI Engineer Jobs

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

This role involves leading AI projects, designing and deploying AI systems, collaborating with stakeholders, and developing tools to enhance efficiency in healthcare applications. The engineer will mentor junior members and iterate on prototypes until widely adopted.

As an Applied AI Engineer, you will build and ship AI features, optimize ML models, ensure production reliability, and collaborate with cross-functional teams.

Lead the design and development of advanced AI agents for cybersecurity, focusing on real-time decision-making and adaptive threat responses, while mentoring teams and collaborating across functions.

Embed with customers to learn their workflows, redesign and build production GenAI/LLM systems on AWS, ship and operate code, evaluate non-deterministic models, and own business outcomes.

The Staff Level AI Engineer will innovate applied AI methods, enhance user experience through AI, and architect AI infrastructure for a SaaS platform, collaborating across product and engineering teams to drive business value.

Design, build, and deploy scalable AI/ML models (especially NLP and predictive models), implement MLOps and data pipelines, optimize model performance, research prototypes, ensure ethical and compliant AI, and collaborate with product, engineering, and data teams to embed AI into the leave management platform.

Design, build, and operate production-grade agentic AI systems and LLM-powered features for financial workflows. Implement RAG pipelines, tool invocation, agent orchestration, vector storage, monitoring, and shared AI infrastructure while ensuring reliability, security, auditability, and regulatory compliance.

The role involves researching, developing, and implementing AI solutions, optimizing models, integrating AI into workflows, and collaborating with teams to enhance business efficiency.

As a Senior Applied AI Engineer, you will design, build, and maintain AI systems using LLMs and backend services, ensuring reliability and performance in production. Responsibilities include developing workflows, monitoring AI behaviors, and collaborating across teams to enhance engineering standards.

The AI Engineer will design, prototype, and deploy RAG pipelines and agentic workflows to process unstructured data for Government and Military clients. Responsibilities include collaborating on architecture and delivering technical reports while ensuring systems are scalable and reliable.

Build production-ready intelligent automations and agentic AI solutions. Full-stack development using TypeScript or Python, LangChain, vector DBs, RAG, and tool-calling. Collaborate with clients, ensure maintainable, tested, scalable deployments, CI/CD, observability, and model fine-tuning or deployment.

Leads technical execution for an embedded AI innovation squad, taking prototypes from discovery through production. Designs reusable agentic AI architectures covering orchestration, tool use, memory, retrieval, integrations, security, and governance. Works with stakeholders to define requirements, makes architecture decisions, develops reference implementations, and establishes measurable outcomes. Provides infrastructure and budget recommendations, resolves complex technical issues, manages delivery dependencies, serves as the primary AI and coding expert, and mentors engineers while evaluating emerging technologies.

Leads technical execution for enterprise AI innovation squads from discovery through production. Designs and governs agentic AI patterns, including orchestration, tool use, memory, and retrieval; integrates AI with enterprise systems; and ensures security, governance, and data-residency compliance. Converts prototypes into scalable reusable solutions, reference implementations, and starter kits. Partners with stakeholders, manages delivery priorities and dependencies, evaluates technologies, measures outcomes, and mentors engineers.

Build and scale production AI systems, APIs, data pipelines, retrieval systems, and model-serving infrastructure for consumer and clinician-facing health products. Integrate and fine-tune LLMs and other models, manage AI feature lifecycles from prototyping through monitoring, and ensure reliability and observability. Collaborate with engineering, product, and science teams. More senior candidates may lead AI architecture, roadmap development, and mentoring.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Design, build, and deploy production ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM features, data pipelines, observability, MLOps, and containerized deployments across cloud and on-prem environments.

As a Lead Software Engineer, you'll design and develop GenAI platforms, integrate LLMs, mentor engineers, and solve technical challenges for scalable solutions.

Lead technical direction and people for an AI/ML engineering squad to design, build, and deploy production-grade ML and Generative AI (LLMs, RAG, agentic systems). Drive MLOps, CI/CD, model monitoring, Responsible AI compliance (HIPAA/PHI), architecture reviews, and mentor 5–10 engineers to deliver secure, production solutions.

Own technical delivery of Moveworks AI Agents for enterprise customers: gather requirements, design and build integrations and automations, tune LLM-based solutions, advise on AI roadmaps, synthesize feedback to product, and create reusable templates to scale implementations.

Develop and optimize scalable backend systems and AI applications, including LLM, GPT, and RAG solutions. Support software project planning, architecture, cross-functional collaboration, deployments, infrastructure, testing, and process improvements. The role requires expertise in TypeScript, Golang, microservices, cloud infrastructure, databases, containerization, DevOps, machine learning frameworks, model optimization, vector databases, and LLM inference stacks.

Design, prototype, and productionize generative AI features and agentic workflows. Build Python services, TypeScript/React UIs, data pipelines, evaluation and instrumentation, and safeguards. Partner with product and stakeholders to measure impact, ensure reliability, and mentor engineers.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.