Top AI Engineer Jobs

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 an AI Engineer, you will create a unified evaluation infrastructure for AI agents, ensuring reliable performance while integrating automated pipelines and feedback systems for rapid model evaluation and quality assurance.

Lead design, deployment, and production scaling of LLM-based and agentic AI systems; evaluate and benchmark models; build prototypes and production AI pipelines; collaborate with engineering and business stakeholders; influence leadership and mentor junior team members.

Design, build, and scale production LLM, retrieval, and agentic AI systems for compliance automation. Implement evidence-grounded, explainable workflows, optimize for latency/cost/reliability, and embed human-in-the-loop guardrails while collaborating cross-functionally.

Build and deploy production-scale AI/LLM systems: own ML lifecycle from model development to serving, optimize GPU/Kubernetes deployments, design LLM applications and APIs with monitoring, apply CI/CD and version tracking, collaborate with product teams, and contribute to AI engineering best practices.

Lead the development and deployment of AI-powered products with a focus on foundational AI systems, LLM optimization, and team collaboration to enhance user experiences in banking.

As a Lead AI Engineer at Capital One, you will design and support AI software, develop LLM and optimization techniques, and collaborate with cross-functional teams to innovate AI products.

As a Senior Lead AI Engineer, you will design, develop, and optimize AI systems while collaborating with a cross-functional team to implement AI-driven products.

As a Lead AI Engineer, you will design and develop AI components, collaborate across teams, improve performance, and contribute to Capital One's AI vision.

The Senior Lead AI Engineer will design and develop AI-driven products, enhance scalable AI systems, and collaborate with cross-functional teams.

As a Senior AI Engineer at Klaviyo, you will design and build scalable backend systems and AI solutions, collaborating with product managers and engineers to enhance AI technologies for optimizing customer experiences.

Drive AI/ML initiatives across storage product development, coordinating engineering, firmware, validation, and data science teams from requirements through deployment. Manage schedules, dependencies, blockers, readiness reviews, portfolio progress, and Go/No-Go decisions. Align priorities with business goals, identify AI/ML opportunities, support adoption of AI-assisted tools, and communicate program status and outcomes to executives and cross-functional stakeholders.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Design, develop, and deploy AI-powered healthcare solutions using Java and Spring Boot microservices. Implement HL7 FHIR and Da Vinci interoperability standards across prior authorization, claims, eligibility, and provider domains. Build Kafka-based event-driven services, integrate relational databases, automate CI/CD, deploy with Docker and Kubernetes on AWS or Azure, and establish monitoring using Splunk, ELK, Prometheus, and Grafana.

Lead the design, development, and deployment of AI-powered healthcare solutions and Java/Spring Boot microservices. Build interoperable clinical platforms using HL7 FHIR and Da Vinci standards, event-driven Kafka components, relational databases, CI/CD pipelines, containers, cloud infrastructure, and observability tools. Address clinical workflows, prior authorization, claims, eligibility, and provider-data challenges while evaluating emerging technologies and promoting responsible AI use.

Lead architecture and development of a firm-wide software dependency risk platform on AWS and Java. Own data ingestion, dependency graph modeling, APIs, integrations, technical strategy, and production delivery. Establish agentic AI engineering practices using tools such as Claude, with governance for security, resiliency, auditability, and human validation. Mentor senior engineers, solve complex technical problems, and represent engineering across security, risk, compliance, and platform stakeholders.

Build and operate production-grade AI, data, and distributed systems for fraud intelligence. Responsibilities include real-time data pipelines, high-throughput architecture, LLM-powered agentic workflows, AI tooling, rule and label automation, ML pipeline productionization, privacy safeguards, and cross-functional collaboration. The role requires strong Python engineering, distributed systems, cloud infrastructure, big data technologies, and machine learning fundamentals.

Lead design, build, and deploy production-scale generative AI and agentic systems: foundation model training, LLM inference, vector search, guardrails, evaluation, observability, and performance optimization across cloud infrastructure.

Design, build, and deploy production generative AI applications (agentic automation) using Node.js and TypeScript. Collaborate with product teams to apply LLMs, RAG, and fine-tuning techniques, manage model training/deployment infrastructure, iterate on real-world data, and monitor/evaluate AI performance to improve user experiences.

Build infrastructure for large-scale AI and agentic workloads, including synthetic data generation, evaluation, orchestration, distributed compute, simulation environments, LLM routing, observability, and developer tooling. The role requires operating nondeterministic AI systems in production, improving reproducibility and reliability, and leading complex engineering initiatives across research, product, and engineering teams.

Leads the end-to-end design, development, deployment, operation, and adoption of AI solutions for Procurement and Supply Chain. Provides technical and people leadership while building LLM, AI agent, machine learning, data pipeline, ontology, integration, and platform solutions. Oversees application support, governance, security, continuous improvement, stakeholder engagement, training, and enterprise adoption. The role also mentors teams, evaluates emerging AI technologies, and translates business needs into scalable technical solutions.

Design, develop, and deploy scalable AI applications and services across the OneStream platform. Build production machine learning solutions, troubleshoot full-stack issues, participate in technical design sessions, and collaborate with cross-functional teams. The role involves applying forecasting, anomaly detection, and natural language processing while maintaining reliable, well-documented code and continuously learning emerging AI technologies.

Own production LLM-driven agent systems for business data enrichment, including classification, web discovery and verification, entity linking, legitimacy scoring, and risk signals. Build shared agent infrastructure, tools, evaluations, tracing, and cost controls. The role requires production Python engineering, browser automation, multi-provider LLM expertise, structured-output design, web scraping, and evaluation methodology. This is a hybrid role based in San Francisco with four in-office days weekly.

Support the design, development, testing, deployment, and optimization of AI and machine learning solutions for legal technology applications. Work with senior engineers on model evaluation, fine-tuning, retrieval and reasoning improvements, troubleshooting, MLOps, documentation, and production-grade enterprise AI systems.

Design, fine-tune, optimize, and deploy enterprise-scale LLMs and NLP systems. Build distributed training pipelines, datasets, evaluation frameworks, RAG pipelines, embedding and vector search solutions, and agentic AI workflows. Optimize GPU training and inference, establish safety and responsible AI testing, collaborate across research and engineering teams, lead architecture reviews, and mentor junior engineers.