Top AI Engineer Jobs

Lead design and delivery of enterprise-scale AI platforms and reusable architectures. Build and scale generative and agentic AI systems, LLM-based automation, and cloud-native solutions while advising executives, ensuring security/compliance, mentoring senior engineers, and driving modernization across the organization.

Lead design and delivery of enterprise-scale AI platforms and reusable architectures, focusing on generative and agentic AI, cloud-native modernization, and production-ready LLM systems. Provide hands-on technical leadership, mentor engineers, ensure security, compliance, and responsible AI practices, and drive cross-organizational initiatives that align AI solutions to business value.

Lead design and delivery of enterprise-scale AI platforms and reusable AI patterns. Provide hands-on principal-level engineering, architecture, prototyping, and technical leadership for Generative and Agentic AI, cloud-native modernization, and cross-enterprise initiatives while ensuring security, compliance, and responsible AI.

The ERP AI Engineer - Manager at PwC leads teams in AI/ML engineering, manages client relationships, and designs AI solutions, all while mentoring team members and addressing complex business challenges.

Lead design and delivery of AI/ML solutions: build and optimize models, transform data into insights, manage projects and budgets, mentor teams, liaise with clients, and uphold technical and professional standards.

Design, build, and deploy end-to-end AI/ML solutions using LLMs, agentic and probabilistic methods. Scope problems, prototype, evaluate and benchmark models, integrate production APIs and cloud infrastructure, apply responsible AI practices, and communicate results to stakeholders while mentoring colleagues.

The Forward Deployed AI Engineer develops AI solutions using LLMs, collaborates with customers on their AI strategy, and executes high-stakes projects to solve real-world problems.

The AI/ML Engineer will design, build, and deploy machine learning systems, optimize workloads, and ensure scalability and compliance of the ITMS platform.

Lead architecture and development of Back-End CAD flow (FlowJam), partner with Physical Design teams to define synthesis/place-and-route/timing and power methodologies for advanced nodes, improve QoR and automation, integrate AI-driven optimization, collaborate with EDA vendors, and mentor engineers.

The Senior Staff AI Engineer will lead AI initiatives, mentor engineers, and ensure the successful delivery of AI solutions for risk management at SoFi, focusing on scalable architectures and innovative AI techniques.

Build and ship LLM-powered production features end-to-end: prompt orchestration, tool calling, retrieval/RAG, embeddings, evaluation infrastructure, observability, safety guardrails, and integrations. Collaborate with clinical teams, prototype quickly, measure outcomes, and raise engineering standards through mentorship and technical leadership.

The Staff Applied AI Engineer will architect multi-agent LLM systems, drive performance optimization, mentor teams, and ensure system reliability while fostering continuous improvement.

The Senior Data Scientist will design and implement AI-enabled products using clinical trial data, mentor team members, and collaborate with stakeholders.

As a Lead AI Engineer, you will develop and support AI products, enhance system performance, and collaborate across teams to innovate AI solutions for Capital One.

The role involves developing and deploying AI-powered products, optimizing performance, and collaborating with cross-functional teams to advance AI technology at Capital One.

Lead AI Engineer responsible for developing AI-powered products, optimizing large language model inference, and collaborating across teams to enhance customer interactions and business value.

The role involves leading AI product development, working with cross-functional teams to enhance AI systems, and optimizing large-scale production AI systems.

Lead AI Engineer responsible for designing and developing AI-powered products, utilizing ML algorithms, and optimizing large-scale AI systems. Collaborate with cross-functional teams to enhance customer experience and drive AI initiatives at Capital One.

Rapidly prototype AI solutions embedded in advisor workflows: integrate models, signals, APIs and UX into experiments, validate feasibility and performance, and hand off documentation to production teams.

Design, train, evaluate, and deploy end-to-end applied AI systems for sports using multimodal data (tracking, video, audio, text). Build event-detection, prediction, and automation pipelines, define metrics and benchmarks, integrate models into real-time products, collaborate with CV, infra, and product teams, and mentor junior engineers.

Design, build, and deploy end-to-end production AI applications for autonomous semiconductor operations. Responsibilities include data pipelines, model training and inference, APIs, front-end apps, digital twin integration, LLM and agentic AI integration, containerized cloud/edge deployments, GPU-accelerated optimization, and monitoring for operational use in manufacturing environments.

Lead AI-powered reporting and insights strategy, designing data architecture, pipelines, and LLM-enabled workflows (RAG, prompt engineering, agentic pipelines). Integrate BI tools and enterprise AI platforms, establish governance for executive AI outputs, and provide technical leadership to scale trusted, automated reporting and self-service insights across the organization.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

Lead design, development, deployment, and support of foundational AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability). Optimize large-scale AI performance (cost, latency, throughput), leverage OSS and SaaS stacks, mentor teams, and shape long-term platform vision.

Lead design, development, deployment, and support of large-scale AI systems (LLM training/inference, similarity search, guardrails, evaluation, observability). Partner cross-functionally, optimize LLM performance (cost, latency, throughput), and shape technical vision and roadmap for foundational AI platforms.