Top AI Engineer Jobs

Build and maintain enterprise-scale ML/LLM infrastructure on Azure: create automated, secure CI/CD pipelines, provision resources with Terraform, enable real-time inference and RAG pipelines, and collaborate with data science and product teams to productionize generative AI solutions.

The Gen AI Data Engineer will design and build distributed data systems, develop data pipelines, manage data infrastructure, and integrate technologies for real-time and batch processing, contributing to scalable analytics solutions.

The Mechanical Engineer will integrate AI with manufacturing by analyzing CAD models, collaborating with data scientists, mapping manufacturing rules, and testing AI systems, ensuring accurate and efficient design-for-manufacturing workflows.

The Mechanical Engineer will apply mechanical design and manufacturing knowledge to support AI integration, perform geometric feature analysis, assist in data labeling, and coordinate cross-functional teams for DFM initiatives.

Senior individual-contributor building production agentic AI and RAG systems end-to-end: document ingestion/OCR, chunking, embeddings/vector indexes, retrieval orchestration, prompt engineering, evaluation harnesses, FastAPI services, database schemas, governance controls, and mentoring mid-level engineers.

Lead architecture and delivery of production-grade agentic AI and RAG systems. Own multi-agent orchestration, embedding/vectorization pipelines, retrieval, evaluation frameworks, and team leadership for engineers building document-intelligence and AI-assisted developer tooling. Responsible for production delivery, observability, CI/CD, and cross-team integration in an enterprise finance environment.

Embed with strategic semiconductor customers to deploy LLM- and agent-based AI into end-to-end EDA flows (RTL through signoff). Architect agentic workflows, integrate Cadence AI products, define KPIs for productivity and ROI, guide secure LLM access and infrastructure, run design reviews, and produce reusable playbooks to scale deployments across sites.

Design and implement highly available, fault-tolerant full-stack web applications and services for Starlink. Build responsive TypeScript/React front-ends, .NET microservices running in Kubernetes, monitoring/alerting, and collaborate across teams to improve architecture, observability, and user experience.

The Senior Vision AI Engineer will develop and deploy computer vision and machine learning models, create optimized vision pipelines, and mentor junior engineers, focusing on industrial applications.

Design, develop, and deploy production-grade AI/ML models and scalable architectures. Lead model training, validation, deployment, monitoring, and cross-functional integration. Conduct research, document methods, and share knowledge to drive business impact and operational reliability.

The AI Analytic Software Engineer-Senior will lead AI and analytics solutions development, ensuring compliance and governance within a federal environment, integrating APIs and building front-end experiences.

As a Senior AI Systems Performance Engineer, optimize and scale foundation models, enhance performance across various system layers, and deliver high-performance AI applications.

Run technical discovery with customers, design and build end-to-end data integrations into Spiral, implement ingestion code and infra-as-code, operate orchestration and monitoring, reconcile and backfill data, and own post-go-live schema and feed changes while collaborating with Sales and CX teams.

The Senior Infrastructure Software Engineer will architect and scale multi-cloud infrastructure, focusing on compliance, observability, and developer efficiency for enterprise AI deployments.

Lead platform automation and AI-driven tooling for Ticketmaster's Global Host Platform. Write maintainable code, build IaC and containerized deployments, design agentic LLM workflows, drive adoption of AI developer tools, reduce tech debt, set SRE standards, mentor engineers, and run on-site trainings and periodic travel to improve reliability and automation at global scale.

As a Site Reliability Engineer for AI platform, you will ensure system reliability, develop automation, manage infrastructure, and evaluate new technologies.

Lead design and implementation of Copilot AI experiences and platform services across Windows, Mac, and mobile. Build secure, performant, distributed systems integrating models and agentic workflows, collaborate with cross-functional teams, ship well-tested maintainable code, and drive iterative product delivery.

Design, build, test, deploy, and maintain cloud-based applications, microservices, and infrastructure. Ensure application performance and quality, fix bugs, write automated tests, and collaborate on new features. Use AI-assisted development tools daily and implement IaC and modern architecture patterns to deliver secure, maintainable code.

Senior AI Software Engineer responsible for designing, developing, and deploying generative AI, LLM-powered, and agentic AI systems. Lead system architecture, implement secure cloud-native deployments and CI/CD, collaborate cross-functionally, monitor and optimize production AI, mentor engineers, and enforce technical rigor and documentation for mission-focused federal solutions.

Design, develop, test, and support scalable AI/ML-powered enterprise applications and microservices. Implement LLMs, Generative AI, and RAG solutions; build RESTful APIs, automated tests, CI/CD pipelines; troubleshoot production issues and collaborate in Agile teams.

Lead architecture and delivery of MUFG's enterprise AI platform: LLM gateways, model routing, RAG, agents, observability, evaluation, governance, and reusable engineering patterns. Provide hands-on technical leadership, cross-functional influence, and mentor teams to enable secure, compliant, scalable AI adoption across the enterprise.

Develop and enhance user-facing features for an LLM observability platform using React and TypeScript. Build reusable components and front-end libraries, translate designs into performant UI code, optimize cross-device/browser performance, and collaborate with backend engineers and designers to improve usability.

Design, build, and operate secure enterprise AI platform capabilities connecting models, agents, and internal systems. Own major infrastructure workstreams (MCP gateway, enterprise context), build backend services and APIs, enable identity-aware governance (access, permissions, auditing), design agentic workflows, and take operational ownership while partnering with stakeholders to scale dependable AI tooling across Spotify.

Embed with strategic customers to design, build, and deploy frontier AI/LLM systems into production. Responsibilities include discovery, architecture, integration, prototyping, deployment, observability, safety evaluations, and driving adoption and product feedback.

Build and maintain marketing data pipelines, replace spreadsheet workflows with structured databases, automate data cleaning/deduplication and syncing, develop AI-driven reports and dashboards, and collaborate with marketing, AdOps, and engineering to ensure reliable, unified, AI-ready marketing data.