Top Remote AI Engineer Jobs

Build AI-driven developer tools: coding assistants, knowledge retrieval, workflow automation, and LLM-powered apps for code generation, understanding, and test automation. Collaborate with engineering teams to identify pain points, ship solutions, and experiment with prompt engineering, RAG, and AI agents.

Design, build, and deploy LLM-based ML solutions using LangChain, fine-tuning, and RAG. Write production-quality Python code (PEP8), containerize with Docker, use Git, test and document work, and apply traditional ML methods like XGBoost and K-means.

Design, build, and deploy production AI/ML and generative-AI solutions (including RAG and agentic workflows) to enhance sales and distribution platforms. Develop scalable components, API integrations, microservices, and automation pipelines; monitor model performance; ensure Responsible AI, security, and enterprise governance; and collaborate with cross-functional and offshore teams to deliver reliable, high-performing systems.

Build and deploy production AI and machine learning solutions using Python, foundation models, traditional ML, retrieval workflows, and synthetic data. Develop models for classification, entity resolution, ranking, prediction, and data enrichment. Own evaluation, MLOps pipelines, model monitoring, deployment automation, performance and cost optimization, and responsible data practices. Collaborate across technical and business teams while working with messy healthcare and insurance data.

Own customer problems from concept through production by building full-stack financial systems powered by AI agents. Develop workflows for invoicing, collections, payroll, reporting, and month-end close; create integrations with banking, accounting, payroll, payment, and communication platforms; and architect secure, auditable, reliable infrastructure. Collaborate directly with founders and finance teams, design intuitive interfaces, iterate based on customer feedback, and help establish the startup’s engineering culture and technical standards.

Design and implement cloud-native AI/ML solutions on AWS. Build LLM and RAG architectures using Transformers, PyTorch, TensorFlow, LangChain. Develop in Python and Golang, contribute to open-source projects, and apply AWS services (Bedrock, SageMaker, ECS, Lambda).

As the Founding GTM AI Engineer, you will create AI-driven solutions to enhance revenue-generating workflows, build automations, and analyze sales data to inform strategies.

Design, build, and deploy scalable AI/ML systems within the OneStream platform. Implement time series forecasting, anomaly detection, and NLP solutions. Troubleshoot full-stack issues, participate in technical design sessions, collaborate cross-functionally, and continuously learn and improve code and AI practices.

Develop and deploy production ML applications within the OneStream platform using Python and SQL/NoSQL. Build scalable services, troubleshoot full-stack issues, participate in technical design, collaborate cross-functionally, and integrate data science solutions into products.

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.

Design, build, and operate production multi-agent AI systems and automation for Marketing Operations. Integrate LLMs with business data (BigQuery, CRMs, Slack), build MCP servers, APIs, and scalable serverless/containerized services, implement observability and governance, and partner with GTM teams to deliver measurable automation and self-service workflows.

The AI Engineer will design and build production systems, focusing on LLM integration, system reliability, evaluation frameworks, and high-quality product features while maintaining strong engineering practices.

Build and operate DV's model layer: fine-tune and distill open-weight models, run on-prem inference infrastructure on Kubernetes, implement a model gateway routing across providers, evaluate model quality/cost/latency, manage GPU provisioning and utilization, and define tooling for model selection and production regression testing.

The Senior Staff AI Engineer will lead the design and optimization of reinforcement learning systems for enterprise applications, focusing on scalable architectures and production deployment. Responsibilities include mentoring, collaboration, and ensuring compliance with AI ethics and safety standards.

The Full Stack AI Engineer will build and deploy AI-integrated MVPs, develop front-end and back-end systems, optimize ML pipelines, and maintain APIs and cloud infrastructure, focusing on healthcare administration.

As a Full Stack AI Engineer, you'll build AI applications and products, prototype solutions, and collaborate with founders in a fast-paced environment.

As a Senior AI Engineer, you will architect AI systems for marketing, build generative pipelines, and ensure compliance with privacy standards, all while leading MLOps processes.

The Forward Deployed AI Engineer will deploy AI solutions, work with clients on integration, and build prototypes while contributing to platform development.

The Senior AI Platform Engineer will lead AI enablement projects across Campminder, collaborating with executives and teams to design AI tools that improve efficiency and empower users.

Lead enterprise model-training strategy for Commercial AI products: fine-tune LLMs (QLoRA/LoRA/PEFT), train traditional ML models, design large-scale data pipelines, enforce data governance and PII handling, build reproducible experiment/training pipelines, optimize GPU/distributed training costs, and mentor teams to operationalize models into production.

Lead design and implementation of the enterprise AI orchestration and retrieval platform: build MCP servers, define tool-surface and multi-agent patterns, design RAG systems and chunking, implement routing/context/window management and observability, set evaluation and safety standards, onboard product teams, and mentor engineers to raise orchestration and retrieval maturity.

The Software Architect will lead AI quality evaluation, design evaluation frameworks, create risk controls, and ensure compliance, partnering with various teams on AI systems.

Design and build multi-agent AI systems (memory, tools, feedback), orchestrate LLMs and RAG, implement REST APIs and full-stack (Node.js/Next.js), optimize databases and cloud infra, integrate embeddings and video classification, lead design-to-deployment, enforce code quality and testing, and collaborate with product and design.

Design and build multi-agent AI systems and RAG pipelines, orchestrate LLMs and embeddings, develop scalable full-stack (Node.js/Next.js) APIs, optimize infra (SQL, AWS/GCP, Docker/K8s), lead technical design-to-deployment, ensure code quality, and implement video content classification and tagging for influencer marketing.

Lead AI technical strategy, architect AI systems, set safety standards, mentor engineers, and evaluate AI models at Midi Health, a healthcare-focused company.