Top AI Engineer Jobs

Design and implement AI solutions, oversee development of agent frameworks, define technical standards, ensure reliability and security of operations, and lead technical strategies for AI integration.

As an AI Engineer at Fieldguide, you'll develop AI agents for complex audit workflows, design systems for reliability, and optimize agent capabilities based on customer needs. You'll build and integrate systems using LLMs and ensure agents improve over time, collaborating closely with product and design teams.

As a Machine Learning Engineer, you will optimize backend services, integrate ML models, collaborate with cross-functional teams, and support continuous improvement initiatives.

The AI Engineer will design, implement, and prototype GenAI and ML solutions to enhance operational intelligence and efficiency in investment services.

The AI Engineer will design and build production-grade AI systems focusing on Retrieval-Augmented Generation and managing Large Language Models. Responsibilities include architecting solutions, integrating vector databases, and ensuring compliance in financial services.

Design and build generative AI features across frontend, backend, and infrastructure. Integrate foundation models, implement search/retrieval systems, ensure reliable production-grade AI behavior, and collaborate with product, engineering, and design to solve user problems.

Lead the technical direction and hands-on implementation of Hilbert's AI stack: design and ship agent-based workflows, production-grade LLM systems, evaluation pipelines, and monitoring. Set architecture and engineering standards, prioritize work, and hire/mentor engineers while collaborating across product, data, and GTM.

Build and deploy LLM and multimodal AI systems that automate drafting, calculations, and documentation for civil engineering projects; develop spatial reasoning models, agents, and production workflows in collaboration with engineering teams.

Design, build, and maintain production-grade AI systems and agent-based workflows; own end-to-end pipelines from experimentation to deployment and monitoring; build evaluation pipelines; collaborate with product, data, and GTM; iterate quickly under ambiguity to shape the AI stack.

Design and implement agentic architectures for a consumer-facing coding AI agent; build evaluation frameworks to measure code quality and intent alignment; deploy and maintain AI/ML systems in production; collaborate on product-focused, TypeScript-based agent development and ship scalable AI products.

Embed Judgment Labs' agent behavior monitoring into customer production systems: integrate monitoring and evaluation into agent workflows, diagnose failures in live environments, guide customers on monitoring and evaluation strategy, and own multiple customer engagements end-to-end to ensure sustained adoption.

Lead data and integration architecture for Federal Civilian customers, consolidating MuleSoft, Tableau, and Data 360 to design AI-ready, compliant solutions. Drive holistic discovery with sales, translate technical solutions into mission outcomes, and own technical wins across multi-cloud environments.

Design and build FLORA's intelligence layer: architect agentic multi-step pipelines, orchestrate text/image/video models, implement memory and retrieval, evaluation frameworks, and collaborate with product, infra, and design to deliver reliable, user-facing creative workflows.

Design, build, and deploy generative AI solutions and APIs that integrate with data platforms. Implement RAG and prompt strategies, automate data workflows, ensure data quality and governance, and document integrations while collaborating with data, IT, and business teams.

Design and develop generative AI applications using Python, build APIs, collaborate with teams, and ensure application performance and reliability.

The AI Lead Engineer will design and develop advanced AI solutions, focusing on agent-based systems, machine learning, and generative AI while providing technical leadership and collaborating across teams.

Lead technical design and delivery of scalable, cloud-native SaaS solutions. Drive architecture and implementation across frontend and backend stacks, mentor engineers, apply AI/agent workflows and NLP, ensure high availability, and collaborate with product and cross-functional teams to ship reliable, production-grade systems.

Lead and manage engineering teams for software delivery, overseeing design, development, and quality processes while ensuring alignment with business objectives and best practices.

As a Product Software Engineer, you will develop and deploy applications, create architecture documentation, collaborate on requirements, and ensure code quality. Responsibilities include coding, testing, and integrating AI-driven features into software solutions.

The Lead AI Cloud Platform Engineer develops and operates Allstate's cloud application platforms, focusing on AI solutions using Azure, AWS, and Google technologies while mentoring junior team members and delivering innovative solutions.

As a Senior AI Cloud Platform Engineer, you will develop and manage cloud platforms for AI applications, utilizing GenAI engineering skills and infrastructure as code principles, while supporting operational tasks and collaborating with teams.

The AI Evaluations Engineer develops evaluation methods and datasets for measuring AI system performance, collaborating across teams to ensure integration and reliability of evaluations.

Lead and manage the design and implementation of AI and SaaS platforms, ensuring security, reliability, and integration within the company's ecosystem while providing technical leadership.

The Senior Cloud Systems Engineer will design, deploy, and integrate a cloud-based AI platform using Python and Azure, managing full deployment lifecycle and backend services.

As an AI DevOps Engineer, you'll develop tools to automate AI workflows, enhance platform productivity, and support critical IT operations.