Top Remote AI Engineer Jobs

Set the technical direction for Rapid7’s LLM and agentic AI systems. Lead architecture, model strategy, fine-tuning, post-training, inference optimization, evaluation standards, and complex cross-team initiatives. Build critical model and inference code, mentor senior engineers, resolve technical trade-offs, and influence product and platform strategy across cybersecurity products.

Sets Rapid7’s company-wide AI technology vision, architecture strategy, and multi-year roadmap for security products. Leads production LLM and agentic AI initiatives, establishes engineering, evaluation, and responsible-AI standards, and guides model and infrastructure investments. Partners with executives, mentors senior engineering leaders, solves high-impact technical challenges, and represents Rapid7 externally. The role spans LLMs, predictive AI, classical machine learning, distributed systems, and scalable AI production infrastructure.

Designs, builds, and ships production LLM and agentic AI systems for cybersecurity products. Leads architecture, model strategy, fine-tuning, post-training, inference optimization, evaluation, and technical roadmaps. Establishes engineering practices, mentors engineers, leads design reviews, prototypes critical model and training code, and partners cross-functionally to deliver scalable, cost-effective AI solutions.

Build secure, production-ready agentic AI applications using AWS and Amazon Bedrock. Develop AI agents, RAG systems, APIs, automations, and enterprise integrations with databases, Jira, Workfront, Oracle, and internal tools. Implement security, access controls, monitoring, testing, governance, and cloud deployment safeguards. Produce maintainable code, document architectures and deployments, evaluate prototypes, and collaborate with AI, security, DevOps, data, and business teams.

Build and deploy secure, production-grade AI solutions for go-to-market teams using retrieval, structured-data querying, and agentic workflows. Partner with GTM leaders to define AI strategy, roadmaps, and build-versus-buy decisions. Establish monitoring, evaluation, security, and ROI frameworks tied to revenue outcomes. Drive adoption through enablement and feedback loops, develop reusable AI architecture and data connectors, improve engineering standards, and mentor other engineers.

Build and maintain AI-powered go-to-market systems that automate sales, expansion, marketing, forecasting, handoffs, and post-sales workflows. Optimize processes, maintain CRM data quality, test and monitor automations, connect GTM tools, and translate operational needs into scalable technical solutions. The role uses AI and iPaaS tools to surface next-best actions, improve deal velocity, and support customer lifecycle operations.

Designs, develops, deploys, and scales advanced AI and machine learning models and systems. Leads research initiatives, architecture decisions, technical design reviews, and continuous improvement efforts. Collaborates with data scientists, software engineers, and product managers, while mentoring engineers and communicating strategies to executive stakeholders. Applies data mining, modeling, NLP, deep learning, and statistical techniques to solve complex product and systems problems.

Own the implementation, hardening, and polish of game AI behaviors and systems for shipping. Collaborate with design, animation, and other disciplines to tune AI features and support the creative vision. Ensure reliable, stable enemy behaviors and enhance existing architecture. The role requires deep Unreal Engine and modern game AI experience, strong C++ skills, animation systems knowledge, and at least one shipped game title focused primarily on AI.

Leads the AI-first engineering vision and roadmap for an enterprise platform. Drives AI-enabled SDLC adoption, generative and agentic AI implementation, intelligent automation, governance, responsible AI controls, model evaluation, security guardrails, and DevSecOps automation. Mentors engineering teams on prompt engineering, RAG, agent frameworks, observability, and AI architecture while serving as a senior technical escalation point for cloud, data, application, and production issues.

Architect and engineer secure, scalable enterprise AI infrastructure for LLM training and inference, machine learning, agentic AI, and production platforms. Lead HPC, GPU, Kubernetes, cloud, networking, storage, reliability, observability, security, and automation strategies. Resolve complex performance and scalability challenges, establish engineering standards, evaluate emerging technologies, lead cross-functional initiatives, and mentor engineers in a regulated environment.

Leads the architecture and engineering of secure, scalable enterprise AI infrastructure. Designs platforms for LLM training and inference, machine learning, agentic AI, and production workloads using HPC, GPUs, Kubernetes, hybrid cloud, networking, storage, and automation. Establishes standards for reliability, security, observability, governance, and compliance; resolves complex performance challenges; evaluates emerging technologies; leads cross-functional initiatives; and mentors engineers.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Define technical standards for HPC, GPUs, Kubernetes, private and hybrid cloud, networking, storage, reliability, security, observability, and automation. Solve complex scalability and performance challenges, guide cross-functional initiatives, evaluate emerging technologies, mentor engineers, and embed governance, compliance, privacy, and responsible AI into secure, resilient infrastructure.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production AI platforms. Define technical standards across HPC, GPUs, Kubernetes, hybrid cloud, networking, storage, reliability, observability, security, and governance. Solve complex performance and scalability challenges, evaluate emerging technologies, lead cross-functional initiatives, and mentor engineers in a highly regulated environment.

Architect and engineer secure, scalable AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Lead HPC, GPU, Kubernetes, cloud, networking, storage, reliability, observability, security, and governance initiatives. Shape enterprise architecture and engineering standards, solve complex performance and scalability challenges, evaluate emerging technologies, lead cross-functional technical programs, and mentor engineers.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and emerging production capabilities. Define technical standards across HPC, GPUs, Kubernetes, hybrid cloud, networking, storage, reliability, observability, security, and automation. Solve complex scalability and performance challenges, embed governance and compliance, evaluate emerging technologies, lead cross-functional initiatives, and mentor engineering teams.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Responsibilities include HPC and GPU architecture, Kubernetes, private and hybrid cloud, high-performance networking, storage, observability, security, reliability, governance, and automation. The role drives technical standards, performance engineering, cross-functional initiatives, emerging technology evaluation, and engineer mentorship in a regulated environment.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. The role covers HPC, GPUs, rack-scale systems, private and hybrid cloud, Kubernetes, networking, storage, observability, security, reliability, governance, and automation. Responsibilities include solving complex scalability and performance challenges, defining engineering standards, evaluating emerging technologies, leading cross-functional initiatives, and mentoring engineers in a regulated environment.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.

Build and deploy production-grade AI/ML software, APIs, and applications for healthcare provider use cases. Develop prompt-based AI capabilities, integrate enterprise data and cloud services, create automated tests and evaluation pipelines, and ensure security, observability, scalability, and reliability. Collaborate with product, architecture, data science, and platform teams while owning technical work from proof of concept through production.

Build and own evaluation infrastructure, observability tooling, datasets, annotation workflows, and diagnostic systems for production AI agents. Lead experiments across prompts, models, routing, caching, cost, and latency. Diagnose failures across planning, tool use, and agent execution, while partnering with AI Core engineering. Manage and grow the AI Quality team as a hands-on player-coach, contributing directly to production code and technical direction.

Designs, develops, and deploys Generative AI applications and scalable AI systems. Collaborates with data scientists and cross-functional teams to integrate models, solve business problems, and ensure application security, compliance, reliability, and performance. The role requires software development expertise focused on machine learning and GenAI, with proficiency in programming languages and ML frameworks. Preferred qualifications include cloud, DevOps, low-code platform, and Greenbelt experience.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

Supports AI engineering initiatives at Vantor, contributing to spatial intelligence solutions and helping develop technologies that enable customers to understand current and future conditions. The posting provides limited role-specific responsibilities but emphasizes problem-solving, innovation, collaboration, and mission-focused work. Applicants must qualify as a U.S. Person under applicable export control and ITAR requirements.

Leads the design, development, and deployment of AI and machine learning solutions for healthcare technology platforms. Builds scalable ML models, MLOps pipelines, LLM and RAG systems, backend services, and agent-based applications. Establishes standards for monitoring, testing, CI/CD, and lifecycle management while collaborating with engineering, data, product, security, and SRE teams. Provides architectural guidance and technical mentorship, evaluates emerging AI technologies, and promotes responsible, secure, and resilient AI implementation.

Leads the design, development, deployment, and operationalization of enterprise AI solutions, including LLM applications, RAG systems, intelligent agents, classification models, and decision-support tools. Establishes prompt strategies, guardrails, evaluation frameworks, monitoring, observability, and governance practices. Partners with Finance, Data Engineering, Architecture, Security, and Responsible AI teams to deliver scalable, secure, compliant, and production-ready solutions.