Top AI Engineer Jobs

Leads the architecture, development, deployment, and optimization of agentic AI applications and autonomous systems. Builds Python backend services, secure APIs, RAG pipelines, microservices, and enterprise integrations using GenAI platforms and LLM frameworks. Establishes CI/CD, testing, evaluation, cloud-native deployment, resilience, and security practices. Provides technical thought leadership, mentors engineers, troubleshoots vulnerabilities and software issues, and manages technology risks and compliance.

Design, build, and deploy production machine learning and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, and embeddings. Own technical delivery from architecture through monitoring and iteration, collaborate with client engineering and product teams, mentor junior ML engineers, and help establish internal technical standards and best practices.

Leads full-stack software engineering for JPMorganChase’s Wholesale Credit Risk technology team. Designs, develops, tests, troubleshoots, and reviews secure production code; improves application resilience and operational stability; promotes responsible AI-assisted engineering practices; automates recurring remediation; and evaluates architectures and technologies with vendors and internal teams. The role requires cloud-native development, agile SDLC expertise, financial services knowledge, data architecture, modern front-end technologies, GraphQL, and API development.

Develop AI and data solutions for aerospace engineering, including machine learning systems, proof-of-concepts, distributed data architectures, technical code, and production applications. Interns collaborate cross-functionally, present architectures and demos, apply cloud and database technologies, and use methods such as Game Theory and DecisionIQ to deliver business value. The role is remote and offers mentorship, project ownership, and potential return offers.

Individual contributor lead role to architect, build, and deploy agentic LLM systems, RAG/hybrid search, and scalable microservices on cloud/Big Data platforms. Owns MLOps/LLMOps, evaluation frameworks, HIPAA-compliant security controls, and integrations with data and internal AI platforms. Partners with data engineering, mentors engineers, prototypes POCs, and drives production reliability and cost KPIs.

Design and build AI agents, first-party web and mobile apps, and scalable backend services. Integrate LLM frameworks and enterprise systems, implement APIs and microservices, ensure observability and security, and collaborate with Finance, Security, and Engineering stakeholders.

Lead development of secure, scalable Java full-stack applications for mortgage technology. Design, develop, test, troubleshoot, review, and debug production code; improve operational stability through automation and remediation. Drive responsible adoption of enterprise-approved AI-assisted development tools, establish validation and security standards, coach engineers, and evaluate vendor and architectural solutions. Collaborate within agile teams using SDLC, CI/CD, resiliency, and security practices.

Lead and mentor an agile engineering team to design, build, test, and operate secure, cloud-native production software. Provide technical direction, deliver high-quality code, drive product design decisions, and advocate firmwide engineering practices while applying AI/ML expertise.

Design and build production-grade GenAI solutions: RAG pipelines, agentic multi-agent workflows, knowledge-base chatbots, vector DBs, LLM integrations, observability, PII redaction, and deploy AI backends with monitoring and documentation.

Architect and build production agentic AI systems for customer support and compliance automation. Lead technical direction for distributed systems, APIs, orchestration frameworks, guardrails, and evaluation systems. Create reusable automation primitives, improve grounding and reliability, reduce hallucinations, and mentor engineers on LLM application design and production readiness. Collaborate cross-functionally to scale AI automation across Coinbase.

Own FloQast’s multi-region AI platform infrastructure across AWS, including Bedrock model runtimes, sandboxed code execution, Terraform, observability, cost controls, CI/CD, security, and reliability. Support Transform, AI Matching, and AutoBuilder by managing scaling, throttling, tenant isolation, journey-based SLOs, model and prompt delivery gates, and AI-specific on-call processes across US, EU, and AU regions.

Own Gusto’s agentic coding platform, including autonomous coding workflows, AI code review, orchestration, security posture, sandboxing, permissions, infrastructure contracts, and AI spending controls. Lead and grow a senior engineering team, recruit and calibrate talent, set the product and technical roadmap, manage ambiguity across multiple domains, and advise executive leadership on AI-native software development strategy.

As an AI Native Software Engineer, you'll build products using AI coding agents, translating customer needs into solutions, and ensuring quality delivery and documentation.

As a Forward Deployed Engineer at Remitly, you will leverage AI agents to build applications, translate customer needs, and evaluate outputs for quality and security, focusing on delivery speed and robustness.

As an Agentic Software Engineer, you'll build and deploy production-grade AI systems, reverse-engineer existing software, guide teams, and facilitate customer discovery.

Lead software engineer responsible for designing, developing, and delivering secure, scalable Java/Spring microservices. Drive team adoption of enterprise-authorized AI-assisted engineering practices, produce architecture artifacts, improve code quality via automation and CI/CD, troubleshoot complex issues, and coach engineers on responsible AI and secure coding standards.

Designs, develops, debugs, and delivers secure, scalable software solutions using modern SDLC and agile practices. Leverages enterprise-authorized AI-assisted coding tools, produces architecture and design artifacts, analyzes large data sets to improve systems, and ensures operational stability and secure coding standards.

Lead and deliver secure, scalable Java-based microservice solutions; drive adoption and safe use of enterprise AI-assisted engineering tools; produce architecture and high-quality production code; improve CI/CD, data models, and system reliability while mentoring engineers and contributing to engineering communities.

Design, build, and deploy AI and machine learning solutions at scale. Develop data pipelines and scalable models, perform data wrangling and analysis, apply NLP and neural networks, collaborate with clients and teams, and contribute to open-source and iterative improvement of AI systems.

The role involves designing and developing AI software solutions, collaborating with cross-functional teams, and optimizing large-scale AI systems for performance and scalability.

Lead the development of AI-powered products by collaborating with cross-functional teams, designing AI components, and optimizing models for performance and scalability.

As a Sr. Lead AI Engineer, you will design, develop, and manage AI software components, working with cross-functional teams to advance AI applications at Capital One.

Lead AI Engineer responsible for creating and deploying AI solutions, collaborating with cross-functional teams to innovate AI-powered banking products, and driving the technical vision for foundational AI systems.

Build and guide scalable full-stack portfolio management platforms using AI-enabled engineering practices. Define technical direction, architecture, specifications, and delivery plans; design resilient distributed systems; contribute to design and deployment reviews; provide production support with an SRE mindset; mentor engineers; establish governance and quality standards; and collaborate globally with product, engineering, and business leaders. The role emphasizes Java, React, Angular, APIs, event-driven architecture, TDD, BDD, cloud-native platforms, and responsible AI integration.

Design, build, and maintain scalable full-stack applications with cloud-native microservices and REST APIs. Integrate AI capabilities (LLMs, RAG, agentic workflows), ensure performance, observability, and reliability, own the full SDLC, and partner with stakeholders to deliver resilient, production-grade solutions.