Top Remote AI Engineer Jobs

Designs and operates production-quality AI framework, runtime, benchmarking, performance, automation, and observability components. Benchmarks, profiles, debugs, and optimizes large language model training and inference across GPUs and Microsoft hardware. The role drives projects from definition through deployment, investigates cross-stack reliability issues, partners with research, infrastructure, model, and hardware teams, contributes to technical standards, and mentors engineers.

Build and deploy production generative AI and machine learning systems for utility clients. Own engagements end to end, including discovery, architecture, retrieval, model selection, evaluation, deployment, monitoring, operator-facing applications, and post-go-live support. Integrate with client data and business systems, establish AI safety and governance controls, manage scope and risk, and communicate with technical teams and executives. Contribute reusable accelerators, evaluation frameworks, and reference architectures across client engagements.

Designs and leads AWS-native data foundations for enterprise AI applications, including knowledge graphs, ontologies, semantic layers, vector indexes, retrieval systems, and LLM grounding datasets. Builds Python and SQL pipelines using AWS services, establishes data models and governance standards, integrates identity-based access controls, and monitors data and retrieval quality. The role also mentors engineers and citizen developers, sets technical direction, and communicates with cross-functional stakeholders.

Design and own the decision-intelligence layer: build models and LLM systems to decide which claims to contest and offer amounts, create evaluation pipelines and golden datasets, architect deterministic vs learned components, close feedback loops from outcomes, and produce explainable recommendations for customers.

Own Soda’s technical content function across its website, blogs, webinars, videos, and audience growth. Build automated, agentic LLM content pipelines with structured outputs and evaluations while maintaining strong editorial judgment, distinct voices, and high-quality technical writing. Develop independently using Python or TypeScript, APIs, webhooks, and databases. Translate data quality and infrastructure concepts for technical audiences, collaborate with founders and customer engineers, and continuously improve workflows based on content performance.

This role involves developing AI-powered analytics interfaces and tools, collaborating with a skilled team to enhance customer data experiences.

The Senior AI Engineer will design and implement personalized agent workflows, memory systems, and evaluate AI agents while collaborating with platform engineering on a proprietary platform.

Design and build enterprise LLM-serving infrastructure on Kubernetes, including GPU scheduling, scaling, model lifecycle management, Helm packaging, offline deployments, platform integrations, and production observability. Contribute across a multi-service Go codebase, create design documentation, participate in reviews, and help guide engineering direction within an autonomous remote-first team.

Lead design and build of production Generative AI solutions (document intelligence, RAG, semantic search). Implement and optimize LLMs, embeddings, and agentic workflows, deploy on Google Cloud/Vertex AI, apply MLOps, monitor performance, and collaborate with clients to translate requirements into scalable, cost‑efficient AI systems.

Design and build enterprise LLM-serving infrastructure on Kubernetes, including GPU scheduling, deployment, scaling, model lifecycle management, Helm-based packaging, offline installations, platform integrations, and production observability. Develop across a multi-service Go codebase, contribute design documentation and reviews, and help establish engineering direction for a remote-first team.

Design, build, deploy, and maintain production AI solutions and agents using Python or .NET on Azure. Implement RAG pipelines, semantic search and vector DBs, work with large-scale data (Databricks/SQL), evaluate model performance, and collaborate with engineers and analysts to productionize experiments.

Lead the design, implementation, and enhancement of LLM-powered AI solutions, collaborating with teams to drive innovation and business outcomes.

The Senior AI Engineer will focus on building AI-driven pipelines, semantic technologies, and enterprise ontologies while mentoring and ensuring best practices in a hands-on role.

As a Senior AI Engineer, you'll develop and deploy AI agents, focusing on model fine-tuning, infrastructure, and user interaction across social platforms, ensuring reliability and engagement.

Design, build, and maintain full-stack web applications with AI/GenAI integration. Develop and deploy ML/LLM models, RAG pipelines, and APIs; implement GitLab CI-based CI/CD; collaborate with data scientists and participate in agile delivery.

Lead the technical direction of AI agents, managing context and workflows while partnering with teams to align AI initiatives with business goals. Oversee architectural decisions and mentor engineers in AI practices.

Develop and maintain microservices-based, real-time software for mission-critical air traffic systems. Collaborate with product, systems engineering, QA, DevOps, and platform teams in an Agile/SAFe environment. Translate requirements into reliable solutions, integrate APIs, databases, messaging systems, and external services, and contribute to design and technical decisions. Apply CI/CD, DevSecOps, automated testing, troubleshooting, performance tuning, and AI-assisted development tools. The role is fully remote and requires Public Trust eligibility and U.S. citizenship.

The AI Solutions Engineer will advise clients on ML and GenAI best practices, demonstrate product value, and drive business reviews and expansions.

Build and deploy AI applications, agents, intelligent workflows, document-processing solutions, predictive analytics, and automation. The role spans AI use-case assessment, solution design, prototyping, evaluation, testing, production deployment, monitoring, governance, and responsible-AI controls. The engineer will integrate AI with enterprise systems, APIs, workflows, and data platforms while partnering with engineering, cybersecurity, governance, and mission teams in a federal environment.

Drive technical sales for Microsoft Azure and AI solutions serving state and local government customers. Lead architecture workshops, demonstrations, proofs of concept, migrations, and modernization initiatives across hybrid and cloud environments. Design secure, scalable, resilient, and compliant infrastructure using Azure services, networking, containers, enterprise applications, and partner technologies. Build trusted customer relationships, resolve technical blockers, collaborate with engineering and partners, and guide adoption of cloud, security, resiliency, and AI capabilities.

Own the complete data science lifecycle for a flagship predictive modeling project. Responsibilities include analyzing and engineering multi-source data, building production-grade pipelines, developing and deploying supervised, unsupervised, reinforcement, deep learning, NLP, and forecasting models, monitoring performance, documenting work, communicating insights, and mentoring junior team members.

Leads Slate’s GenAI strategy, architecture, and production deployment across vehicle engineering, manufacturing, supply chain, software, and go-to-market operations. Owns enterprise-scale GenAI platforms, agentic systems, data and context layers, model serving, evaluation, guardrails, and observability. Applies physical AI to robotics, computer vision, predictive maintenance, and factory feedback loops. Recruits and develops a high-performing technical team while remaining hands-on with coding, architecture, stakeholder engagement, and technical standards.

Build and maintain responsive WordPress websites, landing pages, reusable templates, and digital lead-generation experiences. Use AI-assisted development tools to accelerate coding, publishing, testing, and troubleshooting while reviewing code for quality and security. Implement technical SEO, analytics, conversion tracking, integrations, performance improvements, and QA. Manage themes, plugins, staging, deployment, backups, and website security while supporting scalable publishing workflows and collaborating with marketing and business teams.

Design, build, test, and support automation solutions across business applications using AI, agentic AI, and RPA. Collaborate with product owners and scrum teams to gather requirements, create technical designs, implement code, automate testing and deployments, troubleshoot production issues, document processes, and mentor junior teammates.

The Senior AI/ML Capacity Engineer will develop capacity planning models, design analytical tools, and collaborate with teams to optimize costs and efficiencies.