Top AI Engineer Jobs

Design, build, and deploy cloud-native, agentic AI applications on AWS/GCP. Apply AI and automation to improve developer productivity, design and consume APIs, ensure security and compliance, support generative/agentic AI in production, collaborate in Agile teams, and contribute to architecture and engineering best practices.

As a Software Engineer, you will build AI systems to enhance developer workflows, implementing agents and servers, and ensuring code reliability through evaluation pipelines.

Lead design and deliver AI-enabled data platform capabilities (metadata, lineage, data quality, retention). Drive adoption of enterprise-authorized AI-assisted engineering, build agentic production systems, develop secure production code, automate remediation, establish production SRE controls, and mentor teams on responsible AI and validation standards.

Lead a team to design and deliver cloud-based full-stack microservices and applications, collaborate with product managers, mentor engineers, experiment with emerging technologies (AI/LLM), and ensure solutions meet regulatory and customer needs using modern languages, databases, containers, and AWS services.

As a Senior Lead AI Engineer, you will design and develop advanced AI systems, collaborate with cross-functional teams, and introduce innovative AI techniques while mentoring others.

The Lead AI Engineer will develop AI solutions, partner with cross-functional teams, and optimize large-scale production AI systems using various technologies.

The Senior Lead AI Engineer will collaborate with cross-functional teams to design, develop, and implement AI software solutions, focusing on large-scale production systems and AI model optimization.

Lead design, development, deployment, and support of large-scale AI systems (foundation model training, LLM inference, similarity search, guardrails, observability). Partner with cross-functional teams, introduce LLM optimization techniques, and shape technical vision and roadmap for foundational AI platforms to deliver scalable, responsible production AI capabilities.

As a Lead AI Engineer, you will develop, deploy, and support AI software components, collaborate with cross-functional teams, and optimize performance of large-scale AI systems.

The Lead AI Engineer will design, develop, deploy, and support AI software components, collaborating with cross-functional teams to create scalable AI solutions that enhance customer interactions and operational efficiency.

The Lead AI Engineer will develop and deploy AI software components, optimize AI systems, and collaborate with cross-functional teams at Capital One to enhance customer interactions through AI technology.

Lead AI Engineer to design, build, deploy, and support production-scale vision and foundation-model components (training, inference, vector search, guardrails, evaluation, observability). Partner with cross-functional teams, optimize LLM training/inference for cost/latency/throughput, and help set the technical vision and roadmap for foundational AI systems at Capital One.

Leads the design, analysis, testing, and validation of cooled and uncooled turbine blades and vanes. Develops cooling concepts, drives design-system improvements, supports engine programs, and serves as the primary technical authority. Collaborates on AI and physics-based surrogate models for CFD and thermal workflows, communicates results to stakeholders and customers, leads proposals and testing, and mentors junior engineers.

Develop and deploy AI-enabled applications for global supply chain and sourcing workflows. Translate ambiguous business needs into technical specifications, applications, data pipelines, ontology objects, validation logic, and production-ready solutions. Collaborate with business stakeholders, product owners, data teams, SMEs, and external engineers; validate applications, support user acceptance testing, troubleshoot data and system issues, document technical decisions, and sustain deployed solutions in a regulated environment.

Develops, trains, tests, deploys, and maintains production computer vision and machine learning models for mission-critical applications. Responsibilities include deep learning, transformer-based vision models, sensor fusion, target tracking, GPU optimization, embedded systems, and real-time missile defense software. The engineer collaborates with multidisciplinary space, military, intelligence, engineering, and AI teams to design and implement robust operational capabilities.

Provides technical leadership and architectural oversight for scalable AI-integrated software. Designs agentic AI systems, MCPs, APIs, RAG solutions, microservices, and prompt strategies; decomposes product goals into engineering tasks; reviews human- and AI-generated code; mentors distributed developers; and improves DevOps and CI/CD pipelines with AI-enhanced testing and reviews.

Design, prototype, deploy, and maintain AI-powered applications and services. Build agentic workflows with orchestration frameworks such as LangGraph, apply prompt engineering and RAG, prepare multimodal datasets, evaluate AI models, and select tools for specific use cases. Develop web applications using Next.js, React, and Python frameworks while integrating AI capabilities with existing systems and workflows.

Supports development of AI-powered applications, including chatbots, virtual assistants, knowledge solutions, natural-language interfaces, and RAG systems. Assists with prompt engineering, testing, debugging, documentation, performance improvements, and integration of enterprise knowledge sources. Collaborates in Agile ceremonies, uses GitHub and software engineering practices, explores emerging AI technologies, and contributes to Generative AI projects involving LLMs, intelligent search, and cloud platforms.

Design and deploy production-grade generative AI applications, LLM solutions, and intelligent agents that automate complex enterprise workflows. Build scalable Python systems, RAG data layers, and containerized deployments in OpenShift. Apply rigorous engineering practices, including testing, code reviews, documentation, and CI/CD. Research emerging AI techniques, mentor colleagues, deliver iteratively in an agile environment, and implement responsible AI guardrails to mitigate operational and regulatory risks.

Build and deploy scalable AI-powered web applications and Generative AI workflows. Develop responsive frontend experiences, backend services, AI data pipelines, LLM integrations, and cloud infrastructure. Collaborate with product managers, designers, and AI/ML engineers to translate user needs into product features, optimize application and model performance, and manage cloud deployments. The role requires ownership of production-grade solutions using modern frontend frameworks, backend languages, RAG architectures, vector databases, CI/CD, and AWS.

Lead the design and development of mission-relevant software, build backend services and APIs, mentor engineers, and support technical demos.

Designs, develops, and prototypes AI and machine learning capabilities for operational technology, industrial control systems, and cyber-physical environments. Responsibilities include analyzing OT telemetry and security data, developing AI agents and ML models for threat detection and vulnerability analysis, and deploying secure capabilities in air-gapped or resource-constrained environments. The engineer partners with OT, cybersecurity, data science, and mission teams to transition prototypes into operational national-security capabilities.

Build and teach reliable AI agent systems using LangChain, LangGraph, Deep Agents, and LangSmith. Design hands-on workshops, tutorials, reference implementations, and technical training. Provide customer guidance, create internal agents and tools, communicate customer feedback to Product and Engineering, and stay current with agent engineering practices. The role requires strong Python skills, live debugging and presentation abilities, customer-facing experience, and up to 20% travel.

Lead development of data pipelines, ETL, and analytics for pre- and post-silicon DFT data; apply statistical methods and applied AI/GenAI to solve EDA/DFT problems; design scalable distributed databases; collaborate across teams and mentor junior engineers.

Leads enterprise AI engineering strategy, architecture, platform development, and production deployment across AbbVie. Builds secure, scalable AI services integrated with AWS and enterprise systems; establishes MLOps, testing, monitoring, governance, validation, compliance, and responsible AI practices. Partners with technical, scientific, regulatory, legal, and business stakeholders, evaluates emerging technologies, guides proofs of concept into production, mentors engineers, and advances engineering maturity.