Top Remote AI Engineer Jobs

Design, build, and productionize Applied AI foundations: model access, retrieval/memory, observability, secure data paths, human-in-the-loop workflows, and reusable AI-driven platform capabilities that support product and operational teams.

Provide onsite and remote technical support for NVIDIA Ethernet and AI infrastructure, troubleshoot Linux-based systems, debug networking and interoperability issues, collaborate with engineering and marketing, document support methodologies, and act as primary customer contact, spending at least one week per month onsite.

Build and launch AI-powered user-facing features on a Ruby on Rails backend. Integrate LLMs (OpenAI, Google, Anthropic), own zero-to-one product initiatives, write well-tested maintainable code, and mentor engineers while collaborating with product, design, and cross-functional teams.

Design, build, and maintain automated test suites, evaluation scenarios, metrics, and datasets for AI-powered features. Automate QA workflows with Python and CI/CD, test APIs and ML model outputs, define quality strategy and acceptance criteria, create CI/CD quality gates, mentor teams, participate in code reviews, and document findings.

Lead design, build, and scaling of Agentic AI and industry-specific solution accelerators on Genesys Cloud. Develop reusable multi-agent workflows, RAG-based knowledge orchestration, integrations with enterprise systems, prototypes, and production-grade AI applications. Provide technical leadership, mentor engineers, collaborate with product and industry SMEs, and deliver repeatable solution blueprints to accelerate deployment and time-to-value.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.

Build and ship internal AI enablement tools, workflows, and guardrails to help engineers use coding agents and LLMs safely and productively. Evaluate models and agents, partner with engineering teams, measure adoption and impact, and provide documentation, training, and hands-on support to increase engineering velocity and quality.

Integrate and scale pre-trained LLMs and semantic search into production SaaS applications. Build RAG/vector data pipelines, multi-step orchestration/agents, backend APIs, automated AI evaluation frameworks, and implement security/privacy guardrails.

Lead technical direction and hands-on development of ML and AI Context systems for HubSpot CRM: model development, productionization, retrieval, embeddings, evaluation, monitoring, and cross-team architectural leadership to deliver measurable product and business impact.

Build and maintain full-stack web and mobile-friendly applications, design and manage Airtable backends, implement workflow automations (n8n/Zapier), integrate LLM/AI services (e.g., Anthropic Claude), optimize performance, collaborate with leadership and vendors, and document systems to reduce single-owner risk.

Build and operate next-generation ML data and feature infrastructure, including near-real-time feature computation engines, feature stores, and embedding retrieval systems. Scale data processing, storage, and serving for high-throughput training and low-latency inference. Collaborate with ML practitioners and AI Platform teams to improve feature discovery, sharing, and practitioner productivity.

Design and build scalable training platform infrastructure for large distributed ML model training. Improve performance, reliability, and cost-effectiveness of training jobs. Build APIs/UX for ML engineers, diagnose GPU/communication/memory bottlenecks, and implement observability, fault tolerance, and on-call practices while partnering with ML researchers and infrastructure teams.

Build and maintain internal and customer-facing web/cloud apps and AI automations. Integrate with third-party APIs (QuickBooks, Microsoft Graph, Zoho, Zoom), implement generative-AI features, ensure security, deploy to cloud, and collaborate with leadership and IT. Work independently on design, testing, and maintenance across varied projects.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

The Software Engineer - AI Systems will design and implement systems to coordinate LLMs, build testable software stacks, conduct research, and collaborate with team members to create AI-powered security solutions.

Build and own Bloomerang's AI harness and platform services: agent workflows, tool/model integrations, orchestration, evals, and guardrails. Ship backend services (authentication/identity) primarily in C#/.NET, diagnose distributed systems issues, improve developer tooling and observability, run model evaluations, and lead responsible AI adoption to increase engineering throughput and deliver measurable product outcomes for nonprofits.

Embed with strategic higher-education partners to design, build, and ship production AI and platform integrations (LLM/RAG, LTI) across Cengage products. Own end-to-end deployments, write production code, ensure compliance and accessibility, evaluate AI safety and outcomes, mentor FDEs, and translate field learnings into product roadmap contributions.

Design, implement, and operate a private, secure AI platform and autonomous agent workforce for engineering workflows. Build agents (FastAPI/CLI/chat), integrate MCP and RAG, maintain containerized infrastructure, CI/CD validation, secrets and cost management, and enterprise integrations (GitHub, Jira, Confluence, Teams). Continuously evaluate models, improve agent reliability, and drive platform adoption across low-level systems engineering domains.

Design and build secure, cloud-native AI applications using LLMs, RAG, and Amazon Bedrock to provide AI-assisted search, summarization, and clinical question-answering; integrate with enterprise healthcare systems using FHIR/HL7, implement vector search and prompt orchestration, ensure Fed security/privacy compliance and CI/CD, and support testing, monitoring, and governance for production readiness.

Lead technical strategy and architecture for an AI Data Engine to accelerate ML dataset generation and data warehouse operations. Hands-on staff engineer mentoring engineers, designing scalable storage, vector retrieval, LLM/agentic workflows, and collaborating with ML research and platform teams to deliver petabyte-scale data solutions.

Lead end-to-end AI-first transformation for operations: define problems, design architecture, build agentic systems and automation pipelines, integrate with CRMs and operational tooling, ship production solutions and drive company-wide AI adoption.

Design, build, and deploy LLM-powered agents and automations (using MCP) to integrate with Salesforce, HubSpot, Jira, and other tools. Create analytics dashboards and synthesize product telemetry into actionable insights for CSMs, consult with enterprise clients, and collaborate with Data Engineering to reduce churn and drive product adoption.

Design, build, and deploy production AI agent workflows and LLM-powered systems. Integrate tools, APIs, databases, and internal services; improve agent quality, latency, safety, and cost. Collaborate with product and operations to translate business problems into practical AI solutions and contribute to backend services and evaluation/guardrail patterns.

Direct AI coding agents to build and ship marketing sites, maintain brand voice and design discipline across five brands, own SEO and information architecture, run competitive research, and review/correct AI-generated code and copy to ensure production-quality outcomes.

Develop, test, deploy, and maintain .NET-centric web applications using C#, Angular 17+, and Azure. Build and consume REST APIs, work with SQL Server, source control, CI/CD pipelines, and collaborate on code reviews, debugging, performance optimization, and third-party integrations.