Top Remote AI Engineer Jobs

Design and build data infrastructure and AI/ML systems—extracting, cleaning, moving, and storing data; develop, deploy, and monitor ML and LLM-based products; build APIs; collaborate with stakeholders and manage projects to meet business needs.

Build end-to-end AI integrations in a cybersecurity platform: gather contextual security data, call and optimize AI services, map responses to actionable UI (React), implement error/fallback handling, monitoring, caching, and user feedback loops while working directly with customers.

Design, build, and productionize LLM-based conversational and AI systems for enterprise coaching. Integrate structured and unstructured data, prototype end-to-end services, improve model behavior and observability, and collaborate with product and engineering to deploy scalable, reliable AI-driven coaching workflows.

Owner of the marketing intelligence stack: build warehouse-backed data models, integrate intelligence and CRM tooling, ship CEO-facing GTM dashboards, and design agentic LLM workflows (on Claude) for briefs, content, and account monitoring while ensuring compliance and observability.

Build and own the Databricks-centered AI/ML platform on Azure, drive enterprise AI governance, remediate shadow AI, deliver automation use cases, and advise stakeholders from prototype to production.

Build and maintain production AI/ML models and pipelines for an NDR platform; develop scalable, secure backend services; optimize detection algorithms and reduce false positives; integrate ML features into product architecture; mentor engineers; participate in code reviews, technical strategy, and documentation; operate cloud-native, containerized services and manage infrastructure as code.

The role involves optimizing analog-aware software for neural networks, designing algorithms, improving software quality, and collaborating on hardware validation. Requires experience with complex algorithms and communication skills.

Design and build core backend systems for an agent platform: orchestration, durable execution, memory, tool integration, observability, and evaluation. Create scalable services/APIs, develop retrieval and long-term memory solutions, monitor and improve agent behavior, and help drive technical direction and team growth for AI-driven collaboration features.

Sola seeks a Software Engineer for Desktop Automation to own and enhance their execution platform, focusing on reliability and integrating with AI pipelines.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

The AI Product Engineer will design, build, and ship AI features for clean energy finance while collaborating with various stakeholders to improve transaction processes.

Design and implement a virtual access layer to enable remote use of a physical AI lab, building scalable, secure infrastructure to process 100,000+ documents/day with LLMs, optimize GPU/LLM throughput, provide customizable interfaces, implement authentication/access controls, and lead projects from design to production.

Build and operate an on-prem AI platform (Model-as-a-Service) including RAG pipelines, vector DBs, MCP tooling, agent repositories, CI/CD integration, and guardrails within MRM. Optimize large on-prem GPU infrastructure and translate business use cases into scalable platform services.

Design, test, and document prompts and prompt libraries to optimize AI language model outputs. Collaborate with product, data, and ML engineers to integrate prompt solutions, run A/B experiments, measure performance, and maintain versioned documentation. Support TIBCO-specific implementation and stay current on AI/NLP techniques.

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Design, build, and deploy production-grade ML and AI systems end-to-end, combining classical ML, deep learning, and LLM/agentic integrations. Implement MLOps, monitoring, CI/CD, model serving, vector search/RAG pipelines, and scalable APIs; mentor engineers and drive engineering best practices.

Early-career TechOps role: build dashboards/alerts, script repetitive ticket steps, participate in RCAs, log user pain points and propose fixes, create a lightweight agent (e.g., Telegram bot) with reusable skills, optimize incident-management workflows, and write actionable runbooks to improve support-driven user experience.

Design, build, and deploy production-ready agentic AI and multi-agent systems to automate enterprise workflows. Integrate agents with enterprise platforms (APIs, OAuth2, webhooks), prototype solutions, implement guardrails and observability, and deliver scalable automation primarily for marketing operations. Collaborate with clients and cross-functional teams, provide technical leadership, and produce reusable integration and orchestration patterns.

Design and develop a C++-based system for simplifying and accelerating computing in DL and HPC, alongside evaluating performance and improving library quality.

The role involves researching and developing GPU-accelerated AI workloads, optimizing algorithms, and collaborating with technical experts to improve hardware performance.

As a Founding AI/Data Engineer, you'll design and build AI-driven products, collaborate across teams, and ensure system reliability while enhancing user experiences.

Design and implement full-stack applications for GenAI, build developer tooling, collaborate with product teams, and enhance AI agent delivery.

Design and implement full-stack applications and AI agents to enable rapid development and deployment of AI solutions in critical industries. Mentor engineers and take ownership of projects while applying secure SDLC practices and collaborating with cross-functional teams.

Own and operate the realtime voice stack and call-data platform end-to-end: telephony/WebRTC integration, streaming STT/TTS, realtime voice agents, call-to-transcript pipelines, data warehousing, analytics, and dashboards. Build AI-native workflows, RAG/search, human-in-the-loop flows, and optimize performance, latency, and cost. Mentor teammates and collaborate across product and subject-matter experts.

Build and maintain full-stack product and AI systems: React/React Native frontends, Node.js services, LLM-powered features (agents, RAG), integrations, monitoring, and scalable automation. Ship production features, ensure reliability, and mentor teammates.