Top AI Engineer Jobs

Design and build scalable backend services and AI-powered applications using Node.js/TypeScript or Python. Develop AI agents, agentic workflows, LLM integrations, tool calling, evaluation frameworks, REST APIs, microservices, and event-driven services. Partner with Product and Business teams, provide architecture guidance and code reviews, lead an offshore engineering pod, and optimize backend and AI workloads for reliability and performance. The role is onsite in Rhode Island or Arizona.

Build and lead development of a scalable AI security platform, including foundational services, APIs, libraries, SDKs, and guardrails for AI-powered security capabilities. Design secure, reliable distributed systems with identity, authentication, authorization, encryption, and data protection controls. Partner across security and engineering teams, improve platform performance and developer experience, and guide the architecture and technical direction of the platform.

Own and scale the machine learning platform from training and experimentation through deployment, monitoring, and production support. Build backend services, APIs, data pipelines, model-serving processes, recovery workflows, and reproducible ML infrastructure. Manage multiple production models, external data sources, real-time inference systems, reliability, and operating costs while collaborating closely with ML engineers and contributing to architecture decisions.

Design, develop, test, debug, and maintain scalable software applications and prototypes using Java and Python. Work in a Linux full-stack environment, integrate customer-tailored large language models, design accessible interfaces, connect external requirements tools, troubleshoot performance issues, conduct code reviews, support planning, and mentor junior engineers. The role requires extensive software engineering experience, a bachelor’s degree or equivalent experience, DoD8570 IAT2 compliance, and TS/SCI clearance with polygraph.

Design and operate scalable AI infrastructure across on-premises, hybrid, cloud, and edge environments. Build developer-friendly platforms for model serving, orchestration, inference optimization, resource scheduling, observability, governance, and cost efficiency. Partner with data science and ML engineering teams to create reusable architecture patterns, infrastructure-as-code frameworks, and AI developer tooling. Drive security and compliance workflows, mentor engineers, and support robotics, industrial AI, and other physical intelligence applications.

Design and build Kubernetes-based LLM serving infrastructure, including GPU scheduling, scaling, model lifecycle management, Helm packaging, enterprise and offline deployments, API integrations, and production observability. Develop across a multi-service Go codebase, contribute design documentation and reviews, and help guide engineering direction. The role requires strong Kubernetes, distributed systems, CI/CD, and infrastructure-as-code expertise, with GPU, inference, or adjacent systems experience.

The AI Foundry Engineer translates government business requirements into prompts for AI and agent-based systems. Responsibilities include evaluating and improving generated code and architecture, identifying design and coding issues, testing and validating AI-driven applications, and deploying complete systems. The role requires extensive software, cloud, distributed systems, AI, web development, testing, and prompt-engineering experience, along with eligibility for a government Public Trust.

Design and configure agentic AI solutions for enterprise manufacturing and supply-chain customers during the sales process. Lead process-discovery workshops, map KPIs and data flows, select and configure agents, integrate with systems such as SAP, Ariba, Coupa, and Jaggaer, and present solutions tied to business outcomes. The role requires enterprise process expertise, low-code automation familiarity, quota-carrying sales experience, and strong workshop facilitation and executive presentation skills.

Build and improve AI recruiting agents using modern LLMs, Python backend services, APIs, prompts, RAG, workflows, and agent orchestration. Evaluate model performance, experiment with AI technologies, collaborate with product and engineering teams, and deploy scalable production systems. The role requires Python, LLM and AI API experience, familiarity with agent frameworks and FastAPI, and cloud platform knowledge. Production AI product experience, model evaluation, tool calling, prompt engineering, and open-source contributions are preferred.

Designs, builds, migrates, and operates AWS workloads and platform infrastructure. Responsibilities include cloud architecture, infrastructure as code, security, cost optimization, environment provisioning, lifecycle management, DevOps and CI/CD, container platforms, and AI foundation enablement. The role uses Terraform, CloudFormation, AWS CDK, Python, Docker, Kubernetes, and numerous AWS services while applying the AWS Well-Architected Framework.

Build AI agent harnesses, research workflows, evaluations, and integrations that enable Claude to conduct science and engineering research. Connect simulation tools, define verifiable tasks, prototype with expert partners, deploy pilots, and convert successful engagements into reusable systems. Collaborate with research and product teams while communicating model capabilities, limitations, and tradeoffs to technical and nontechnical stakeholders.

Build and ship production agentic systems for Roku’s OS Platform UI, including multi-step agents, internal engineering tools, real-time telemetry processing, and hardware-in-the-loop capabilities. The role requires strong systems or infrastructure fundamentals and ownership of AI-generated code through design, review, and quality gates. Engineers work across platform, application, UI, cloud, and physical device environments at streaming scale.

Design, build, and operate backend infrastructure for ambient healthcare AI, including audio ingestion, inference orchestration, distributed workflows, APIs, storage, observability, and cloud systems. Improve reliability, scalability, latency, and cost across real-time and asynchronous workloads. Lead incident response, establish SLOs, develop recovery and debugging tools, partner across engineering and clinical teams, and mentor engineers on distributed systems and operational practices.

Leads AI enablement for Global Quality by translating business and quality-process needs into prototypes, requirements, validation plans, and scalable governed solutions. Builds AI-enabled decision support, retrieval, workflow automation, dashboards, and agentic tools; defines data, knowledge, testing, and governance requirements; manages vendors; and partners with technology, data, cybersecurity, and Quality teams. The role also develops executive presentations, business cases, roadmaps, process maps, and handoff packages for enterprise adoption.

Design, build, and maintain large-scale data pipelines and feature engineering workflows to support fraud and cyber-attack detection models. Improve model deployment, monitoring, drift detection, and MLOps automation while partnering with data scientists, engineers, and product teams to productionize AI solutions and enhance operational reliability and performance.

Architect and develop scalable, event-driven backend services, APIs, orchestration systems, and data-context management solutions for AI agents supporting laboratory workflows. Integrate cloud infrastructure, scientific instruments, and software systems while establishing data contracts, integration standards, observability, security, and resilience. Prototype emerging AI technologies and mature successful solutions for regulated production environments. The role requires extensive distributed systems experience and supports R&D software teams in San Jose or Indianapolis.

Designs, develops, deploys, and supports enterprise automation, endpoint-management solutions, AI-assisted tools, and low-code applications. Responsibilities include PowerShell automation, SCCM and endpoint management, Microsoft Power Platform development, AI agent and connector creation, integrations with Microsoft 365 and APIs, testing, troubleshooting, documentation, security compliance, and operational support.

Lead development of AI agent products and AI-native applications for hardware engineering workflows. Prototype ideas rapidly, build products end to end, improve agent user experiences, establish interaction patterns, and lead technical direction across product engineering. Partner with design, product, and platform teams while working closely with engineers and users.

Build and rapidly prototype AI-native product features for hardware engineers. Develop full-stack interfaces, APIs, streaming agent workflows, citations, editable outputs, and trustworthy human-agent handoffs. Own product decisions from concept through customer delivery, observe real users, and iterate on usability. The role requires strong front-end development with TypeScript and React, experience with AI-powered interfaces, rapid prototyping, product ownership, and modern AI coding-agent workflows.

Designs, deploys, and maintains AI-driven web applications, services, and agentic workflows using Anthropic APIs and Claude. Develops Python REST APIs and React/TypeScript front ends, creates prompt and LLM evaluation frameworks, and manages testing, debugging, security, observability, CI/CD, Docker, and cloud deployment. Optimizes SQL queries and collaborates with business teams to deliver reliable, cost-effective AI solutions.

Build and operate real-time voice AI agents for Call Guard. Responsibilities include designing conversational systems, reducing latency across speech and language components, integrating frontier models and agent frameworks, defending against voice-cloning and adversarial attacks, and maintaining telephony, VoIP, and cloud infrastructure. The role also involves architectural ownership, scalable backend development, cross-functional product collaboration, code reviews, and mentoring while delivering privacy-focused consumer products.

Own full sales and partnership cycle to bring voice AI to mobile service robots: engage senior robotics decision-makers, qualify and prioritize opportunities, close deals, and shape the commercial playbook while operating autonomously within a small technical team.

Builds and deploys end-to-end generative AI solutions, translating business needs into scalable, secure, and maintainable architectures. Responsibilities include AI/ML observability, LLM gateways, cost controls, rate limiting, security guardrails, self-service deployment frameworks, CI/CD integration, and containerized ML workloads. The role collaborates with infrastructure and cross-functional teams while evaluating emerging AI capabilities and supporting enterprise AI initiatives.

Design and build AI agents, intelligent automations, governed data products, and scalable HR technology integrations across Workday, Databricks, and enterprise systems. Develop secure generative AI solutions using LLMs, RAG, tool calling, orchestration, and APIs. Architect HR data pipelines, domain models, metadata, and governance practices. Partner with HR, data, security, and engineering teams to deliver production solutions, establish engineering standards, and mentor HR technology engineers in AI and modern data engineering.

Own the applied AI layer of a software platform by building production AI services, retrieval systems, structured extraction pipelines, evaluation suites, guardrails, and agentic workflows. Manage model performance, reliability, latency, cost, and monitoring. Translate product goals and user feedback into measurable experiments and shipped improvements while leading complex projects and maintaining dependable live systems.