Top AI Engineer Jobs

Build and operate a scalable AI security platform, including infrastructure, APIs, SDKs, distributed systems, and security primitives. Design identity, authentication, authorization, encryption, data protection, and AI-agent safety controls. Partner with security and engineering teams to create reusable platform capabilities, improve reliability and developer experience, and shape the architecture of Robinhood’s AI Security platform.

AI Engineer Interns will preprocess and analyze data, support machine learning model development and optimization, help deploy AI solutions, document experiments and workflows, and research emerging AI methods. The role requires collaboration with senior engineers and cross-functional AI team members. Interns will work on ecommerce technology problems in a hybrid, full-time summer internship in Lehi, Utah, with potential for a future full-time role or part-time extension.

Lead design, development, deployment, and maintenance of large-scale Python-based generative AI platforms and services. Collaborate with data science, technical, and business stakeholders to define architecture, deliver production APIs, enforce Python standards and testing, drive CI/CD and monitoring, mentor developers, manage vendor partnerships, and ensure governance, risk, and controls alignment for enterprise GenAI solutions.

Researches, designs, develops, deploys, integrates, evaluates, and tests AI/ML and Generative AI solutions for mission-focused applications. Builds LLM-based systems using retrieval-augmented generation, agentic workflows, cloud services, and AI/ML frameworks. Participates in the full software engineering lifecycle, collaborates with distributed Agile teams, and communicates technical concepts to technical and non-technical stakeholders.

Architects and builds WGU’s conversational AI platform, including retrieval, inference, orchestration, tool calling, security, privacy, guardrails, evaluation, observability, and APIs. Develops scalable production services in Java and Angular on AWS, establishes shared standards and platform capabilities, and provides technical leadership, mentoring, documentation, and production support. Requires extensive software engineering experience and hands-on production experience with LLM applications, RAG, tool calling, and AI evaluation.

Build and operate Python backend services and AWS infrastructure for autonomous identity operations agents. Responsibilities include durable workflows, enterprise connectors, PostgreSQL and vector retrieval, model API integrations, analytics pipelines, usage reconciliation, deployment automation, observability, tenant isolation, audit controls, and recovery testing. The role owns production reliability and safe integrations with Okta, AWS, ServiceNow, and other enterprise systems.

Own end-to-end delivery of low-code AI solutions for customers, including discovery, workflow design, CRM and communication-tool integrations, AI Agent configuration, deployment management, and performance optimization. Lead cross-functional projects, create reusable frameworks, train internal teams and partners, support outbound adoption campaigns, and influence product strategy. The role also owns regional revenue targets and collaborates with GTM, Product, Engineering, Sales, and Customer Success to scale AI adoption.

Develop and optimize GPU kernels, compilers, runtimes, and distributed inference systems for large language model serving. Improve latency, throughput, reliability, and hardware efficiency through profiling and low-level performance optimization. Integrate scalable improvements into production systems, collaborate across model, infrastructure, compiler, and hardware teams, and mentor engineers. Principal-level contributors additionally lead cross-stack initiatives and create reusable performance capabilities across models and hardware generations.

Design and develop high-scale distributed AI infrastructure services supporting model training and inference on Azure Machine Learning. Build backend services using C#, Go, Rust, and C++ on Kubernetes and Docker. Operate reliable, secure, low-latency systems with 24/7 availability, provide operational ownership, collaborate with engineers and researchers, and provide technical leadership.

Design, build, and operate scalable cloud platform services for deploying, monitoring, governing, and managing AI models and workloads. Lead architecture, reliability, security, observability, automation, and developer self-service initiatives using distributed systems and SRE practices. Collaborate across engineering, product, and research teams, analyze production performance, drive continuous improvement, and mentor engineers while influencing technical strategy.

Build production LLM systems and agentic workflows for enterprise process automation. Develop context-learning libraries, autonomous knowledge-management agents, continuous learning loops, evaluation and confidence systems, and scalable AI orchestration. The role combines applied research with production engineering, requiring hypothesis-driven experimentation, reliability improvements, and contributions to AI strategy and product capabilities.

Lead design and delivery of a scalable AI platform for Aladdin: build Kubernetes-based model deployment, cloud-native infrastructure, CI/CD for ML, ensure security/compliance, troubleshoot platform reliability, translate business needs into robust technical designs, and collaborate across global teams.

Leads the design, implementation, and production deployment of AI-powered solutions across technology teams. Responsibilities include architecture, LLM and agent workflow development, observability, telemetry, evaluation frameworks, guardrails, governance, reliability, cloud integration, and operational readiness. The role evaluates AI technologies, defines reusable patterns, partners with engineering and business stakeholders, supports scalable AI infrastructure, communicates technical decisions, and mentors engineers.

Evaluate, configure, integrate, and develop AI and enterprise technology solutions for legal and business operations. Translate stakeholder needs into technical requirements, test applications and AI workflows, build prototypes, automate processes, develop API integrations, troubleshoot data flows, and support implementation. The role requires maintaining reliable, secure, scalable solutions while documenting findings and collaborating with legal, engineering, IT, security, data, and application teams.

Build and extend Skydio’s AI coding-agent platform, including agent integrations, MCP servers, sandboxed execution, secure permissions, secrets handling, and auditability. Develop infrastructure for internal tools and agents, collaborate with firmware, autonomy, cloud, and application teams to improve workflows, measure adoption and engineering impact, and drive internal adoption of AI development tools.

Build and operate production AI systems that transform written briefs into interactive web assets and personalized multimedia content. Responsibilities include agent orchestration, model selection, automated quality evaluation, generative video pipelines, recommendation and ranking integration, performance feedback loops, backend infrastructure, reliability tooling, and technical architecture. The role requires taking language-model and autonomous-agent systems from development through dependable production deployment at scale.

Design, build, deploy, and scale production-grade AI agents and intelligent systems. Lead technical workstreams, develop agentic workflows, establish evaluation and monitoring practices, make engineering trade-offs, and partner with product and business stakeholders. Mentor engineers, conduct reviews, improve system reliability, evaluate emerging AI technologies, and transition successful solutions to long-term technology teams.

Design, build, and scale production-grade AI agents and intelligent systems from experimentation through deployment and support. Lead technical workstreams, develop agentic workflows, establish evaluation and monitoring practices, make architecture and model trade-offs, and create reusable AI engineering patterns. Partner with product owners and business stakeholders, mentor engineers, conduct design reviews, manage technical debt, and guide successful AI solutions toward long-term operational ownership.

Build and ship production-grade AI products from problem framing and data pipelines through deployment and iteration. Develop agentic solutions with city partners, lead technical scoping workshops, and contribute to predictive modeling, real-time decision-making, and LLM-powered experiences. Design scalable APIs, backend systems, and distributed data flows while collaborating with product, data science, and operations teams. Help establish AI Labs’ technical culture and identify high-impact AI opportunities.

Leads research, design, development, testing, validation, and deployment of AI/ML prototypes for space and missile defense. Collaborates with customers and multidisciplinary stakeholders to define requirements and present solutions, mentors junior engineers, tracks milestones and budgets, supports proposals and thought leadership, and advances scalable AI/ML algorithms using large datasets.

Leads research, design, development, testing, validation, and deployment of AI/ML prototypes for space and missile defense. Interfaces with customers and stakeholders, translates mission requirements into technical solutions, mentors junior engineers, tracks milestones and budgets, supports proposals, and contributes to technical thought leadership. The role requires Python-based algorithm development, AI/ML expertise, large-scale data experience, and eligibility for a U.S. Government Security Clearance.

Provides technical pre-sales and consulting support for Microsoft’s GitHub, Visual Studio, Azure AI, and cloud developer platforms. Leads demos, proofs of concept, hackathons, code-with sessions, and architecture workshops for enterprise development teams. Advises customers on secure, scalable cloud-native architectures, DevOps, CI/CD, and AI-assisted software development; resolves technical objections; collaborates with engineering; and promotes Microsoft solutions through customer and developer communities.

Drive technical sales and solution adoption through demonstrations, proofs of concept, hackathons, and architecture workshops. Partner with enterprise platform and engineering teams to design secure, scalable solutions using Microsoft Azure, Dynamics 365, Power Platform, and AI capabilities. Resolve technical blockers, influence solution architecture, support production deployments, build customer relationships, and represent Microsoft through technical thought leadership.

Lead architecture and hands-on development of reusable customer-facing AI platform services, including conversational AI, agent orchestration, retrieval, grounding, memory, tool integration, and policy enforcement. Define APIs, data models, deployment designs, testing, evaluation, and operational requirements. Build production systems across GCP and Azure while driving reliability, security, observability, CI/CD, and technical standards. Provide staff-level influence through architecture reviews, mentoring, code reviews, and cross-functional leadership without direct reports.

Build and lead development of an agentic AI platform for developer productivity: design, implement, test, and ship features; use and advocate AI tooling; experiment with AI approaches; mentor engineers; ensure quality, scalability, and cross-team collaboration.