Top AI Engineer Jobs

The Senior Engineer, Enterprise AI designs and develops AI applications that enhance productivity and decision-making across T-Mobile's enterprise, utilizing various AI frameworks and collaborating with cross-functional teams.

Design and build Hazel's evaluation platform, focusing on AI quality, data pipelines, and creating measurable evaluation criteria through collaboration with financial experts.

Embed with client teams to map and redesign creative workflows, build and deploy AI-powered pipelines integrated with DAMs/CMS/marketing stacks, create proofs of concept, establish governance, and train teams to adopt scalable content production automation.

Embed with client teams to diagnose operational friction, redesign operating models, and build AI-powered proofs of concept. Map workflows, establish governance and measurement frameworks, scope engagements, drive adoption, and deliver working prototypes to demonstrate measurable business value.

Lead design, development, and production deployment of enterprise AI agents and LLM-powered tooling to automate engineering workflows. Define technical vision, set standards, mentor engineers, partner with stakeholders, and ensure reliability, observability, and scalability of agentic systems.

Develop and maintain AI tooling systems for space stations, focusing on frontend customizations, AI integration, and full-stack feature implementation.

Design, build, and scale AI-powered automation using LLMs, RAG, vector DBs, prompt pipelines, and agent systems. Integrate with internal tools and cloud platforms, implement orchestration, observability, security guardrails, measure impact, and partner cross-functionally to replace manual processes with reliable production-grade automation.

Operate and support production AI infrastructure across global datacenters, focusing on high-performance NVIDIA GPU platforms, Kubernetes, and high-speed networking. Monitor and troubleshoot infrastructure, network, hardware, and platform incidents; participate in incident response and root cause analysis; improve observability, automation, and operational runbooks.

The role involves designing, training, and evaluating ML models, optimizing performance, and collaborating with teams to implement AI technologies in products.

Design, build, and productionize end-to-end AI and generative systems that convert unstructured content into structured knowledge. Develop retrieval, semantic search, and knowledge-driven pipelines; ensure evaluation, reliability, explainability, and ongoing monitoring. Collaborate cross-functionally to align AI solutions with business priorities and optimize for latency, cost, and throughput.

As an AI/ML Engineer, you'll implement machine learning techniques, build AI models, and support national security applications, collaborating with various teams.

Design, build, and deploy production-grade AI/ML systems (NLP/LLMs, computer vision, generative AI). Create scalable data pipelines, microservices/APIs, and cloud- or edge-deployed models. Ensure security, observability, and performance; perform model optimization, versioning, A/B testing, and documentation. Collaborate with engineers, mission SMEs, and product teams to meet operational requirements.

Develop and prototype AI-driven applications, validate ideas with real data, and transition projects into production while collaborating with teams and customers.

Design and deliver derivatives hedging and financing solutions by building pricing, risk, and scenario models; develop data pipelines and analytics platforms; create AI-driven agentic workflows and knowledge tools; integrate AI with internal systems for real-time analytics; support front-office OTC derivatives, client pitches, and proprietary platform development.

Design and lead development of ML and AI solutions: explore, clean, analyze structured and unstructured data, build predictive models, apply NLP/text-mining techniques, integrate enterprise systems, visualize results, and advise clients while guiding teammates.

The AI & Data Integration Solution Engineer will collaborate with executives, customers, and partners to implement AI solutions for US Federal clients, focusing on data integration and analytics.

Build and scale an enterprise Knowledge Graph platform and semantic pipelines to power AI-driven features. Design graph models, semantic routers, vector/RAG workflows, cloud-native data pipelines, and AI developer tooling. Own feature delivery, ensure code quality, mentor engineers, and collaborate across product, ontology, and engineering teams.

Develop algorithms in machine learning and statistics to address complex challenges. Design data pipelines and deploy solutions using cloud infrastructure.

Lead a Labs team as a hands-on engineer building hybrid AI/optimization systems: design ML-augmented optimization, prototype and productionize solutions, architect MLOps/CI-CD, benchmark solvers, mentor engineers, and collaborate with researchers and stakeholders to scale reusable components and APIs across Kroger.

Lead architecture and implementation of Vumedi's AI discovery platform: design personalized recommendation systems, semantic search, retrieval and ranking pipelines, vector/embedding-based search, RAG/knowledge-graph integrations, scalable inference and retrieval services, and evaluate emerging AI technologies while partnering with product, data science, and engineering to deliver production LLM-powered discovery for physicians.

The role involves owning product areas, working on backend, frontend, and with LLMs/ML models, delivering high-quality experiments, and engaging customers.

Manage and optimize cloud-based streaming platforms, ensuring high availability and performance for real-time analytics. Collaborate across teams to enhance data pipelines and integrate AI solutions.

Manage deployment, versioning, and lifecycle of AI models across multi-cloud platforms. Define reusable AI patterns (RAG, orchestration, prompts, vectors), build embedding and retrieval pipelines, establish CI/CD and IaC, ensure telemetry and governance, collaborate with security and vendors, and provide platform documentation, training, and technical leadership.

The AI Platform Production Engineer will automate deployment and management of AI workloads, ensure system reliability, and enhance tooling for production services, leveraging extensive engineering expertise.

The role involves leading AI-augmented development practices, building AI/ML cybersecurity solutions, mentoring team members, and collaborating cross-functionally to enhance software delivery using advanced technologies.