Top AI Engineer Jobs

Design, develop, deploy, and support 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. Partner with engineers, scientists, technical program managers, and product managers while contributing to technical vision, roadmap development, and responsible AI practices. Lead and mentor engineering teams and apply emerging AI research to production systems.

As a Senior AI Engineer, you'll develop ML and Generative AI models, ensure integration with enterprise applications, and manage large datasets while collaborating with teams and mentoring others.

Own the AI-first product vision and roadmap for Legal & Compliance enterprise applications. Design and deliver generative-AI features, AI agents, MCPs, and RAG-powered workflows across CLM, matter management, e-signature, GRC, and e-discovery. Translate legal needs into functional specs, ensure security, privilege protection, and SOX-aligned controls, run POCs and vendor evaluations, and drive adoption, change management, and metrics for AI-enabled legal operations.

Lead design and deliver cloud-native microservices and AI/LLM-driven features. Architect scalable, secure systems, drive AI-assisted engineering practices, improve observability/automation, mentor teams, and integrate generative AI into business workflows.

Design, code, test, and maintain applications using Kotlin, Rust, and SwiftUI. Implement ATDD and CI/CD, build API integrations and microservices, manage application lifecycle and project planning, conduct code reviews and debugging, collaborate with clients, and apply analytical thinking for business process improvements.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

Design, build, and maintain AI agents and automated marketing workflows. Map automation opportunities, deploy and monitor agents, integrate with marketing tools, produce impact reporting, ensure data governance, and document/enable marketing teams.

Lead development and evaluation of AI products for medical professionals using large-scale datasets. Create product and client-facing analytics, influence data team strategy, and collaborate with product managers, analysts, and engineers to execute data projects from ingestion through analysis to recommendations.

Lead and grow engineering teams building distributed, agentic AI systems and frameworks. Drive architecture, scalability, reliability, and operational excellence across cloud-native, high-throughput services while partnering with cross-functional stakeholders.

The Senior ML/AI Engineer will enhance developer productivity for AV engineers at GM by designing, implementing scalable, reliable systems and mentoring team members.

Design, build, test, deploy, monitor, and maintain production LLM-based agentic solutions for Commercial Customer Success. Integrate agents with CRMs and Samsara systems, define evaluation frameworks and success metrics, implement automated tests and monitoring, iterate on models and prompts, and demonstrate impact on customer outcomes and retention.

Lead design, development, deployment, and support of large-scale AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize LLM performance and scalability, partner with cross-functional teams, and contribute technical vision and roadmap for foundational AI systems.

Lead design, development, deployment, and support of large-scale AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and optimization to improve scalability, cost, latency, and throughput while guiding technical vision and cross-functional delivery.

Lead the development and implementation of Physical AI systems, focusing on robotics and computer vision, while optimizing data pipelines and supporting AI platforms. Collaborate with cross-functional teams and mentor other engineers to drive best practices.

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 design, build, and operate scalable GenAI platform components including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Collaborate cross-functionally, invent LLM optimization techniques, and shape long-term technical vision for foundational AI systems.

Own product-layer performance for agentic AI systems in healthcare. Design prompting, retrieval, context, memory, tool use, evaluation, escalation, and human-review workflows. Build production-grounded evaluations, diagnose failures, improve accuracy, reliability, latency, cost, and steerability, and approve model changes. Partner with Product and Engineering while maintaining model cards, intended-use guidance, limitations, and failure-mode documentation for safe, transparent deployment.

Leads the design, development, deployment, and support of foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Develops optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost. Partners with research, engineering, product, and program teams; contributes to technical vision; and mentors engineers while applying emerging AI research in production.

Build and operate production machine-learning systems, including LLM and generative AI applications, scalable APIs, MLOps pipelines, model observability, prompt management, vector search, and real-time or batch inference. Develop Python and SQL backend services using FastAPI, asynchronous workflows, cloud platforms, and containerized deployment. Integrate Azure OpenAI and multimodal models while supporting security, cost optimization, testing, incident management, and client-facing consulting engagements.

Design, build, test, deploy, and support scalable full-stack, AI-enabled applications and platform services. Create custom AI agents, integrate LLMs/embeddings/search, implement MCP-based integrations, ensure reliability, security, observability, and lifecycle support for production systems.

Leads the design, development, deployment, and optimization of scalable AI and generative AI solutions. Translates business needs into secure, production-ready architectures; applies foundation models, NLP, retrieval, vector search, and agentic workflows; and implements MLOps and LLMOps practices. Manages project workstreams, evaluates solution performance, ensures security and compliance, collaborates with stakeholders, and coaches junior team members.

Build and deploy AI-powered solutions across Sales, Marketing, and Customer Support. Diagnose workflows, identify constraints, evaluate whether AI is appropriate, and own initiatives from discovery through deployment. Develop LLM applications using prompt engineering, RAG, agentic architectures, orchestration, guardrails, and enterprise integrations. Measure business value through flow metrics, adoption, ROI, and feedback loops while partnering with technical and non-technical stakeholders.

The role involves designing, developing, and supporting AI software components, leveraging various AI technologies, and optimizing performance for large-scale AI systems.

As a Senior Lead AI Engineer, you will develop AI solutions, partner with cross-functional teams, optimize LLM performance, and contribute to the AI vision at Capital One.

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.