Top AI Engineer Jobs

As a Full Stack Engineer, you'll develop platform capabilities, collaborate with teams to implement solutions, and integrate new technologies while ensuring architecture and security are sound.

Design and deliver AI-driven features end to end, including autonomous and background agents, LLM orchestration, retrieval, tool integrations, interactive user interfaces, memory systems, and evaluation infrastructure. Build reliable, auditable systems that measure factual and subjective quality while collaborating with an in-person engineering team to ship production software.

Architects, operates, and improves cloud data and analytics platforms and BI infrastructure. Owns platform standards, CI/CD, reliability, monitoring, incident response, security, governance, performance, and technical documentation. Leads onboarding and enables engineering and client teams through reusable patterns and guardrails. Requires production platform ownership, cloud data and BI expertise, accounting experience, scripting, SQL, troubleshooting, and cloud fundamentals.

Build and operate a secure, scalable enterprise AI development platform. Develop Kubernetes and cloud-based tooling, Terraform modules, APIs, containerized workloads, and reusable Generative AI solutions. Design agentic orchestration, vector-store integrations, authentication, observability, deployment automation, and reliability practices. Establish architecture standards and decision records while supporting AI adoption across the firm. Collaborate globally in Agile teams and participate in on-call operations.

Owns infrastructure, deployment, reliability, and observability for multi-cloud AI systems and autonomous agents. Responsibilities include Terraform-based infrastructure across AWS, Azure, and GCP; managed Kubernetes; incident response; SLOs; on-call operations; compliance automation for HIPAA and SOC 2; customer environment provisioning; and developer deployment platforms. The role also operates observability tooling and supports production AI, inference, and research-to-production workflows.

Build and scale AI/ML solutions for healthcare revenue cycle automation. The role owns AI outcomes, identifies high-value use cases, shapes technical and product roadmaps, converts prototypes into scalable production systems, and architects infrastructure for training, testing, monitoring, security, and deployment. The engineer will apply ML, NLP, and LLM approaches to claims and workflow optimization while balancing technical tradeoffs, business value, cost, and operational efficiency.

Lead customer deployments of Kubernetes and vCluster on bare-metal GPU infrastructure, configuring GPU nodes, networking, storage, RDMA, and InfiniBand. Validate production-ready managed Kubernetes environments, troubleshoot complex infrastructure, transfer operational knowledge, document reusable deployment playbooks, and provide feedback to Engineering and Product. Support pre-sales proof-of-value engagements and help AI cloud and factory customers independently operate and scale their platforms.

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.