Top AI Engineer Jobs

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 foundational AI systems including LLM training and inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI performance (latency, throughput, cost), collaborate cross-functionally, and contribute to technical vision and roadmap for production AI infrastructure.

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.

Design, implement, and operate production-grade agentic AI and LLM-enabled services on AWS. Build retrieval-augmented systems, APIs, and integrations; optimize performance, scalability, cost, and observability; implement evaluation, testing, guardrails, and secure development practices while collaborating across product and engineering teams.

Lead architecture and hands-on delivery of production-grade LLM and agentic AI platforms: design cloud-native AWS services, retrieval and RAG, observability, evaluation frameworks, secure APIs, performance/cost optimization, and mentor senior engineers while defining engineering standards for reliable, safe AI operations.

Lead and mentor a team to design, build, and operate agentic LLM systems and production LLM-backed APIs. Own end-to-end ML lifecycle, MLOps, inference optimization, vector memory, safety/guardrails, and cross-functional governance for scalable, secure AI deployments.

Lead and guide agile teams to design, build, and deliver secure, scalable AI-enabled software. Develop and review production code, drive product and technical decisions, champion enterprise AI-assisted development practices, implement RAG/LLM architectures and vector stores, integrate cloud services and CI/CD, and establish responsible AI guardrails and validation standards.

Lead an agile engineering team to design, build, and deliver secure, scalable AI-enabled products. Provide technical guidance, write and review production code, drive product and architecture decisions, adopt enterprise AI-assisted development practices, implement RAG and LLM solutions, and ensure responsible AI guardrails and evaluation frameworks.

Lead design and implementation of agentic AI workflows and integrations for Acquia DAM. Build, ship, and observe production AI features (auto-tagging, search, governance), set AI engineering standards, evaluate models and tooling, mentor engineers, and represent AI architecture to product and customers.

Design, build, and deploy production-grade AI and Generative AI solutions. Collaborate with data scientists and platform engineers to implement scalable, observable models, manage ML experiments, automate deployments, and review code. Prototype new AI capabilities and apply LLM/GenAI patterns (RAG, agentic workflows) for enterprise integration.

Take Deepgram's speech models and optimize, compile, and run them on resource-constrained embedded and edge hardware. Work across model compression, runtime performance (C/C++/Rust), RTOS/bare-metal and embedded Linux environments, vendor NPUs/DSPs and inference runtimes, benchmarking, deployment pipelines, OTA updates, and partner with silicon vendors to enable real-time, low-power on-device voice experiences.

Lead end-to-end delivery of AI solutions (ML, Generative AI, agentic systems), design and deploy production-grade LLM/RAG and multi-agent architectures, define responsible-AI standards and evaluation, mentor engineers, and partner with stakeholders to measure and drive business value using cloud AI platforms and enterprise CI/CD/IaC.

Lead end-to-end delivery of production AI solutions (ML, GenAI, agentic systems, RAG) on the enterprise AI platform. Design, prototype, deploy, monitor, and evaluate solutions; set technical standards; mentor engineers; and partner with stakeholders to drive measurable business value and adoption.