Top Remote AI Engineer Jobs

Provides technical pre-sales and consulting support for Microsoft’s GitHub, Visual Studio, Azure AI, and cloud developer platforms. Leads demos, proofs of concept, hackathons, code-with sessions, and architecture workshops for enterprise development teams. Advises customers on secure, scalable cloud-native architectures, DevOps, CI/CD, and AI-assisted software development; resolves technical objections; collaborates with engineering; and promotes Microsoft solutions through customer and developer communities.

Build scalable data pipelines, automation workflows, internal tools, and AI-powered quality-control systems. Lead data collection projects for frontier AI labs, establish quality frameworks, improve customer support processes, and manage cross-functional execution. Collaborate with technical and non-technical stakeholders to solve ambiguous operational challenges and deliver datasets and systems that improve advanced AI models.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Design, build, and deploy scalable AI/ML solutions and end-to-end ML pipelines (ingestion to deployment). Implement MLOps/LLMOps, RAG/LLM applications, agentic workflows, model monitoring, and integrate solutions into production using cloud CI/CD.

Design, develop, and deploy end-to-end machine learning and large language model solutions. Perform data discovery, cleaning, preprocessing, and visualization. Build/train LLMs and Agent+RAG systems, collaborate with stakeholders and AI teams, and apply graph-based analysis to support enterprise integration and analytics.

As a Senior AI Enablement Engineer, you'll design AI infrastructure, build scalable systems, ensure compliance, and promote AI adoption across teams.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Design, build, and maintain production-grade AI-powered go-to-market automations (prospecting, lead scoring, campaign orchestration, attribution). Integrate ML/LLM tools into marketing and sales workflows, track business-critical metrics, ensure reliability and scalability, and collaborate with GTM strategists and non-technical stakeholders to drive measurable pipeline and revenue outcomes.

Build and deploy LLM-powered agents, skills, and platform capabilities across the company. Own the full lifecycle from design and implementation through evaluations, rollout, and ongoing improvement. Define specifications and measurable quality evaluations, partner with the Applied AI product manager, make independent product decisions, and collaborate with sales, revenue operations, editorial, and people operations to identify and automate workflows.

Develop scalable backend services and APIs while integrating Generative AI models into enterprise applications. Responsibilities include fine-tuning and optimizing LLMs, building microservices, implementing RAG pipelines, deploying applications to cloud platforms, and managing CI/CD, Docker, and Kubernetes workflows. The role also involves performance monitoring, testing, code reviews, debugging, and collaboration with engineering teams.

Conduct open-ended AI research focused on end-to-end training workflows using LanceDB. Design replicable experiments and benchmarks, demonstrate workflows across AI domains, develop content, publish research and models, and collaborate with engineering and product teams. The role requires deep learning expertise, experience training state-of-the-art models, open-source software development, strong prioritization, product awareness, and staying current with AI research.

Build software infrastructure for large language model pretraining and post-training, including benchmarking, evaluation, routing, and backend systems. Contribute to model architecture, data curation, training, inference, alignment, and RLHF. Gather insights for compute roadmaps, make data-informed architectural decisions, and deliver iterative AI products in a fast-paced environment.

Lead technical pre-sales for enterprise AI applications: run demos, PoCs, hackathons, and architecture workshops. Design secure, scalable cloud-native AI solutions, resolve technical blockers, and advise customers on AI Foundry, Azure AI, Responsible AI, and AI management/security to accelerate adoption and deployment.

Own the company’s AI-powered quality and release infrastructure as its first dedicated hire. Build agent-run test harnesses, LLM graders, regression coverage, release gates, fresh-install and upgrade verification, and natural-language UAT. Manage CI health, artifact publication, deployment pipelines, credentials, certificates, and staging environments. Write production Python code, convert escaped bugs into regression tests, and provide trusted release-readiness evidence through dashboards, runbooks, retrospectives, and operating policies.

Lead technical pre-sales for enterprise healthcare data and AI deals. Own discovery, solution design, RFPs, security reviews, proof-of-value programs, data-delivery scoping, and technical evaluations. Configure integrations with Snowflake, Databricks, APIs, and CRM systems; explain healthcare claims data and AI capabilities to diverse customers. Build demos, reusable enablement resources, and scalable architecture patterns while partnering with commercial, product, data, and engineering teams.

Build end-to-end frontend and backend features for a voice AI trust platform while working directly with enterprise customers. Responsibilities include customer discovery, debugging, proofs of concept, product decisions, reusable platform development, and post-sales technical support. The role requires TypeScript and Python proficiency, AI coding tool fluency, strong product judgment, clear communication, and approximately 10%–20% travel.

Build and operate Sysdig’s AI platform, including MCP servers, agent infrastructure, AWS Bedrock runtime and gateway, usage tracking, governance, observability, security controls, and employee AI automations. The role requires production ownership, AWS platform engineering, full-stack development, LLM application experience, and strong autonomy in designing secure, scalable AI systems.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Design and deliver LLM-powered, agentic AI systems to automate customer support and compliance. Build scalable, secure backend infrastructure in Python and Go, integrate third-party models/APIs, implement grounding and guardrails, and measure performance. Lead end-to-end projects, mentor engineers, conduct code reviews, and participate in on-call and incident response to improve resolution times and reduce operational costs.

Design, build, and deploy AI-driven solutions and RAG pipelines; lead technical design and Azure deployments; integrate AI agents with enterprise systems; ensure governance, scalability, CI/CD, and mentor junior engineers.

Develop end-to-end AI-powered applications, including React/Next.js interfaces, Python or Node.js backend services, APIs, and agentic workflows. Integrate LLM APIs and real-time streaming experiences, design scalable systems, write comprehensive tests, participate in code reviews and on-call support, and contribute to architecture. Collaborate with designers, product managers, and ML engineers to deliver customer-facing products.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

As a Principal AI Engineer, you'll design scalable AI systems, mentor teams, develop large-scale LLMs, and optimize AI-driven cybersecurity solutions.

The Associate Director, AI Engineer will design and develop multi-agent systems, collaborate with teams, and implement AI workflows for clients.

The AI Engineer will develop LLM-powered healthcare applications, improve documentation platforms, build inference pipelines, and collaborate with teams to launch features, all aimed at reducing clinician burnout.