Top AI Engineer Jobs

Own the roadmap and execution for W&B Models, an experiment-tracking platform for AI researchers. Improve experiment organization, training visualization, monitoring, evaluations, and large-scale analysis across web and mobile. Conduct user research, translate ML workflows into product decisions, collaborate with engineering and design, and use data and feedback to launch polished, high-performing experiences. The role requires deep familiarity with ML research, technical product management, data visualization, AI tools, and developer or researcher audiences.

Lead the design and delivery of enterprise AI platform capabilities and production agentic AI solutions. Build multi-agent systems, RAG and knowledge management solutions, cloud-native infrastructure, and MLOps/LLMOps capabilities. Partner with business, architecture, security, legal, and risk stakeholders; influence architecture decisions; mentor engineers; and establish reusable engineering, governance, monitoring, and operational practices.

Build enterprise AI platform capabilities and production AI applications, including agentic workflows, retrieval-augmented generation, semantic search, vector databases, cloud-native services, and AI model operations. Collaborate with senior engineers on orchestration, developer tooling, governance, security, responsible AI, monitoring, testing, and compliance. Use Python, Google Cloud Platform, Kubernetes, CI/CD, and infrastructure as code while developing scalable AI solutions.

Designs and delivers enterprise AI platform capabilities and production-ready agentic applications. Builds multi-agent systems, RAG and knowledge platforms, orchestration frameworks, cloud-native services, and developer tooling. Implements MLOps and LLMOps for deployment, evaluation, monitoring, observability, and optimization. Partners with stakeholders, contributes to architecture and engineering standards, and independently owns initiatives from design through production deployment.

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.

Lead the design, development, deployment, and support of foundational AI systems, including LLM training and inference, similarity search, guardrails, evaluation, governance, and observability. Develop optimization techniques that improve scalability, cost, latency, throughput, and hardware utilization. Partner with engineering, research, product, and program teams while contributing to AI platform strategy, technical vision, and long-term roadmaps.

Lead the design, development, testing, deployment, and support of foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale production AI systems for scalability, latency, throughput, cost, and hardware utilization. Collaborate with engineering, research, program management, and product teams while shaping the technical vision and roadmap for responsible AI platforms.

Leads end-to-end delivery of production AI, generative AI, and agentic AI solutions. Designs architectures using multi-agent orchestration, RAG, hybrid retrieval, and human-in-the-loop workflows; establishes evaluation, testing, observability, safety, and responsible AI practices. Partners with business and technical teams to measure value, mentors engineers and data scientists, defines engineering standards, and influences enterprise AI strategy and roadmaps.

Design, develop, deploy, and scale enterprise AI products, agents, automations, and intelligent workflows for the People Organization. Build multi-agent systems integrating Workday, ServiceNow, Microsoft Copilot, and other enterprise platforms through APIs and event-driven architectures. Apply HR domain expertise to employee lifecycle processes, ensure secure and responsible AI governance, and implement monitoring, testing, observability, and evaluation frameworks. Partner with business and technology leaders to prototype AI solutions and drive adoption and measurable workforce transformation.

Owns the strategy, architecture, and implementation of production AI and machine learning systems for personalization, search ranking, relevance, and patient-provider matching. Builds recommendation engines, retrieval models, LLM applications, and scalable ML infrastructure while establishing AI safety, evaluation, and MLOps standards. The role requires hands-on model development, technical leadership, mentoring, and responsible deployment of AI in a regulated mental healthcare environment.

Develop and run rigorous benchmarks and profiles for LLM inference workloads to identify bottlenecks and drive targeted performance optimizations across frameworks, runtimes, and GPU hardware. Design experiments (quantization, caching, speculative decoding), validate changes against real traces, partner with platform engineers to productionize improvements, and produce clear technical writeups to guide model-serving, runtime, and hardware decisions.

Lead design, development, deployment, and support of scalable GenAI platform components (foundation model training, LLM inference, vector search, guardrails, evaluation, observability). Optimize training and inference performance, contribute to technical vision and roadmap, and partner with cross-functional teams to deliver responsible, production-grade AI systems.

Lead modernization of enterprise AI/ML infrastructure and analyst workflows. Build agentic, scalable, secure platform services, integrate generative AI and low-code/no-code tooling, scale AutoML for tabular and time-series, and engineer automated governance and controls while mentoring teams and influencing cross-functional stakeholders.

Lead design, development, deployment, and support of large-scale AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability). Partner cross-functionally, invent LLM optimization techniques, and contribute to technical vision and roadmap for foundational AI infrastructure.

This role involves leading the development of AI-powered products, optimizing large scale AI systems, and mentoring cross-functional teams. It focuses on designing and deploying AI components, contributing to the technical vision, and applying cutting-edge AI techniques.

Lead design, build, deploy, and support of foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize production LLM performance (cost, latency, throughput) and contribute to long-term technical vision.

Lead development and deployment of foundational AI systems: design, build, test, and support LLM training and inference, similarity search, guardrails, observability, and optimizations for scalability, cost, latency, and throughput while shaping technical vision and collaborating with cross-functional partners.

Lead design, development, deployment, and support of foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, and observability. Optimize large-scale AI performance (cost, latency, throughput), partner cross-functionally, and contribute to technical vision and roadmap.

Lead cross-functional teams to develop and deploy AI-powered products, improve AI system performance, and create innovative AI solutions at Capital One.

The Senior Lead AI Engineer will develop and support AI software components, collaborate with cross-functional teams, and enhance banking experiences through AI systems.

The Lead AI Engineer will design, develop, and support AI software components, leveraging various AI technologies to enhance banking services. The role involves partnering with cross-functional teams to deliver AI-powered products and optimize large-scale production AI systems.

Design, develop, test, deploy, and support production AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Develop state-of-the-art optimization techniques to improve scalability, cost, latency, throughput, and hardware utilization. Shape the long-term technical roadmap for foundational AI platforms while collaborating with engineers, research scientists, program managers, and product managers. Lead and mentor engineering teams and influence senior cross-functional stakeholders.

The Lead AI Engineer will design and implement LLM-powered applications, optimize RAG pipelines, and evaluate models while integrating AI systems into production environments.

The Senior Engineer collaborates with teams, designs and implements technical features, writes production-ready code, and ensures high-quality deliverables, while also engaging in research and learning on new technologies.

Design, develop, deploy, and support scalable AI platform components, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for latency, throughput, cost, scalability, and hardware utilization. Contribute to foundational AI strategy, collaborate cross-functionally, and lead or mentor engineering teams.