Top AI Engineer Jobs

Own and operate OnRamp's AWS platform and deployment systems, build CI/CD and agent infrastructure, apply LLMs and coding agents to automate DevOps, ensure observability, reliability, security and compliance (SOC 2/HIPAA), and create developer-facing automation to increase team velocity.

The Lead AI Enterprise Data Engineer will architect scalable data platforms, develop AI solutions, and create real-time dashboards, while overseeing AI model deployment and integration with existing systems.

Build end-to-end Ambient AI products across the full software stack, ship rapidly, work with LLM-powered clinical documentation and billing systems, collaborate with customers, and ensure production reliability (alerting, metrics, SRE).

Build end-to-end Ambient AI products for healthcare, work across the software stack, and engage directly with customers to resolve issues.

Build and ship AI-driven features for post-sales workflows; prototype quickly; develop platforms for prompt execution, search infrastructure, and LLM observability; create AI evals and scorers; own projects end-to-end and collaborate with PMs, designers, and engineers.

As a Senior Software Engineer focused on AI agents, you'll develop core agent architecture, handle complex workflows, and work with large sales data to automate processes and support the sales cycle.

Design, build, and deploy AI-powered web applications end-to-end. Collaborate with customers to define product specs, implement front-end and back-end systems, build scalable data pipelines, integrate sensor data, optimize performance, mentor junior engineers, and maintain production deployments across government and industrial clients.

Join Artian AI as an AI/ML Engineer to develop their innovative AI platform, focusing on enterprise process automation through agentic AI. Responsibilities include working with AI Workflow Agents, multi-agent systems, and various LLM technologies.

Partner with customers to design and deliver production-ready AI/ML solutions, drive POC-to-production success, troubleshoot technical issues, inform product roadmap with customer feedback, and communicate outcomes to stakeholders.

The role involves enabling enterprise-wide adoption of Microsoft AI and Automation tools, developing scalable platforms, and ensuring compliance with regulations. Responsibilities include platform strategy, architecture, agent development, and governance in a highly regulated financial environment.

As a Full Stack Engineer, you'll develop features for backend services and frontend interfaces, design architectures, and collaborate with clients. You'll own the complete lifecycle of features from ideation to deployment.

The Staff Machine Learning Engineer will optimize the inference layer for voice applications, focusing on model serving and real-time audio processing. Responsibilities include driving performance architecture, collaborating with model partners, and developing evaluation frameworks for STT and TTS models.

This role involves optimizing model serving layers for voice AI applications, working with inference engines, and improving performance for STT and TTS systems.

The Lead Product Engineer will design and deploy enterprise applications, drive Azure migrations, and collaborate with cross-functional teams in an AI-driven environment, mentoring junior engineers and implementing best practices.

The role involves architecting and implementing scalable AI services, enhancing system reliability, and collaborating on software architecture for AI-driven search features.

The role involves building and integrating full-stack features for an AI chat application, focusing on reliable workflows and real-time AI interactions.

As a Software Engineer, you'll enhance AI Engine performance, optimize app features, and collaborate on GPU/CPU optimizations with hardware partners.

The Staff Software Engineer will design and develop scalable cloud services, focusing on AI infrastructure, with hands-on system architecture, mentoring, and engineering culture shaping responsibilities.

Design, build, and improve ML systems and infrastructure, mentor junior engineers, and write maintainable code in a customer-focused environment.

Lead architecture and technical direction for Scale's Generative AI Data Engine, embedding into high-priority Contributor teams to unblock delivery, overhaul platform infrastructure for LLM-native workloads, ensure scalability/reliability, mentor engineers, partner with product/leadership, and own large end-to-end backend, frontend, and ML system initiatives.

Design, deploy, and operate infrastructure and tooling for ML and LLM workloads. Build containerized AI services, CI/CD pipelines, and IAC using Terraform. Manage cloud and on-prem environments, ensure reliability, observability, security, and scalability of production AI systems. Integrate AI tooling with data and internal systems, prototype new technologies, troubleshoot issues, and document runbooks.

Design, prototype, and operationalize AI/ML solutions (including LLMs and RAG) for a USG program. Curate data pipelines from enterprise financial and scheduling datasets, lead LLM training/fine-tuning, scout commercial AI technologies, draft SOWs, evaluate contractor deliverables, and advise on secure deployment and model lifecycle management in high-security environments.

Build and operate core AI agent infrastructure: orchestration, context management, filesystem and sandboxed execution, model routing and serving, databases, Kubernetes, networking, and cloud systems to make long-running agent workloads fast, reliable, safe, and cost-efficient.

Partner with customers to architect, build, and deploy high-scale production AI applications. Translate ambiguous goals into reliable, observable services, implement end-to-end model deployment and monitoring, optimize ML projects, and own customer-facing technical delivery and product outcomes.

Design and build agentic AI systems for cybersecurity: architect AI cyber taskforce and production-grade agentic components, implement ranking/retrieval/search powered by LLMs, design training/evaluation frameworks, decompose security workflows, address scale/performance, and mentor engineers while contributing to engineering and operational excellence.