Top AI Engineer Jobs

Senior Software Engineer focused on AI infrastructure performance insights and observability. Responsible for designing and building monitoring, instrumentation, metrics, and tooling to measure and optimize compute and platform performance while collaborating with infrastructure and ML teams.

Drive technical sales for Azure cloud and AI infrastructure solutions. Lead architecture sessions, workshops, demos, proofs of concept, and migration or modernization initiatives. Design secure, scalable hybrid-cloud architectures for Windows, SQL, Linux, PostgreSQL, Oracle, and enterprise applications. Advise customers on networking, resiliency, compliance, containers, AKS, Azure Arc, SAP, VMware, and partner technologies while collaborating with engineering and business stakeholders.

Supports applied AI projects from client discovery and strategy through solution development, testing, implementation, monitoring, and documentation. Builds AI-powered workflows, automations, custom GPTs, prompt workflows, data pipelines, and integrations using Python, APIs, low-code tools, and AI services. Collaborates with consultants, engineers, sales, and clients while explaining technical concepts clearly to non-technical audiences. Researches emerging AI technologies and helps deliver reliable, production-ready business solutions.

Lead applied research and engineering for feedback-driven agentic AI: define reward modeling and reinforcement learning roadmap, design production learning loops and evaluation frameworks, run large-scale experiments, and ship research into production while partnering across product and engineering.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

The Staff Software Engineer will lead the architecture and design for AI workload orchestration on Kubernetes, mentoring engineers and driving technical initiatives across teams.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.

Lead design and delivery of production-grade AI capabilities to improve developer productivity across engineering tools, CI/CD, and embedded software workflows. Define technical vision, integrate AI into toolchains, drive productionization, establish responsible AI standards, mentor engineers, and influence cross-functional roadmaps and implementations.

Design, implement, and deploy ML models for autonomous driving (detection, tracking, trajectory generation). Apply advanced techniques (imitation learning, RL, foundation models), collaborate cross-functionally, and support production ML systems and model deployment.

Lead end-to-end product builds by orchestrating AI coding agents to create applications, automations, and basic agents. Translate customer problems into deployable solutions, instrument usage and outcomes, evaluate AI output for security/privacy/quality, engage with customers to find automation opportunities, and document prompts, patterns, and approaches for the team.

Build and scale Klaviyo's AI-native Customer Agent platform across frontend and backend. Design conversational experiences, retrieval and orchestration systems, integrations, observability, and deployment. Drive architecture, mentor engineers, ensure reliability, and ship high-quality, scalable AI-driven customer engagement features.

Lead design and delivery of enterprise integrations, automation, and AI-driven workflows across SaaS platforms. Architect scalable APIs, middleware, and conversational self-service; partner with Security, Infrastructure, and Product to reduce operational toil and enable reliable, extensible enterprise platform capabilities.

Design, build, test, deploy, and maintain cloud-based web applications and microservices. Ensure performance, reliability, and code quality while collaborating on new features, debugging, and optimizing systems. Use AI development tools daily, contribute to infrastructure as code, and promote maintainable architecture and documentation.

Own Greenboard’s quality strategy as its first dedicated Automation Engineer. Build the test framework, automated regression suites, QA practices, release gates, and critical-path validation for compliance workflows. Develop methods for evaluating nondeterministic AI outputs using golden datasets and quality thresholds. Validate data integrity across large-scale ingestion pipelines, conduct bug triage, support release sign-off, and collaborate closely with engineers and regulated financial-services customers.

Designs and implements AI/ML capabilities for cyber and intelligence workflows, including LLM-powered and agentic automation. Captures mission-user workflows, integrates AI systems with APIs and enterprise data stores, and delivers reliable solutions for classified environments. Collaborates with analysts, operators, developers, and leadership to improve analytical efficiency, data accessibility, and decision-making while meeting security and governance requirements.

Build production-grade AI features and LLM pipelines (RAG, prompt engineering, context management) for financial-data workflows. Implement agentic workflows with Agno, optimize for accuracy/latency/cost, evaluate output quality, and integrate AI capabilities with backend APIs while maintaining code quality, observability, and testability.

Lead customer-facing deployments of agentic AI for chip design: onboard and integrate ChipAgents, prototype full-stack solutions (backend models to frontend), collaborate on model and infrastructure improvements, create docs and dashboards, and remain hands-on with code, prompt engineering, and workflow orchestration to drive adoption and iterate from field feedback.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

Lead design, development, testing, and deployment of production-grade agentic AI systems. Architect integrations with enterprise systems, drive advanced analytics and experimentation, mentor engineers, and own model lifecycle, reliability, and innovation for agentic/autonomous solutions.

Design and operate Procore’s next-generation cloud compute and data platform in a FedRAMP-compliant environment. Build and support distributed, highly available systems; manage Kubernetes clusters, cloud-native infrastructure, and infrastructure as code; participate across the software development lifecycle; run technical proofs of concept; and collaborate with product, UX, IT, and engineering teams to shape platform capabilities and roadmap.

Build and deploy AI-powered tools and workflows across performance marketing, creative production, CRM, landing pages, growth operations, and product growth. Automate analysis, personalization, content generation, campaign activation, reporting, and conversion optimization. Integrate LLM APIs, marketing platforms, data pipelines, and orchestration tools while mentoring growth teams on AI adoption and partnering cross-functionally to deliver production-grade solutions.

Provides operational support and issue resolution for AI infrastructure and platform services. Manages Kubernetes and Docker deployments, monitors distributed workloads, and develops Python and Shell automation. Uses GitLab CI/CD, JIRA, ServiceNow, and Azure AD while troubleshooting networking and storage issues. Documents infrastructure processes and runbooks, collaborates cross-functionally, and participates in periodic on-call rotations, including occasional weekend coverage.

Own presales discovery, solution architecture, technical demos, prototypes, proof-of-concept definition, technical evaluations, RFP responses, and security questionnaires for enterprise AI customers. Translate customer workflows and technical requirements into validated architectures and measurable business value, manage implementation risks, and provide accurate delivery handoffs. Convert field insights into reusable reference architectures, demos, and product improvements.

Build and maintain scalable data platforms and pipelines that integrate structured and unstructured data. Retrieve, merge, and analyze data from APIs and databases using Python, R, and SQL. Prepare datasets for AI and generative AI models, evaluate machine learning performance, and guide multidisciplinary teams in agile client projects supporting fraud detection, cancer research, and national intelligence missions.