Top AI Engineer Jobs

Lead architecture and development of production-grade agentic AI and multi-agent systems for reliability engineering. Design autonomous agents, manage AI infrastructure and deployment, ensure observability/security/guardrails, automate remediation, evaluate vendors and models, and produce secure, high-quality production code to improve system reliability and engineering productivity.

Own, build, and optimize Spark-based production data pipelines and ML data lake to support recommender systems and personalization. Scale cluster configurations on GCP Dataproc, ensure data quality and accessibility, support ML engineers for model development, resolve performance bottlenecks, and collaborate on platform architecture to deliver reliable data products that drive business value.

The Principal Engineer, AI/ML Architecture will define the technical direction for Coupa's AI platform, focusing on model training, evaluation, safety standards, and mentoring a team of ML and data engineers.

Lead design and delivery of AI-driven product solutions across the commerce platform. Own high- and low-level architecture, implement scalable RAG/LLM systems, select AI infrastructure, mentor teams, and collaborate with product and engineering leadership to execute AI strategy and rapid experimentation.

Build and improve a realtime multimodal agent (Aidan) across voice, vision, browser interaction, and a self-improving context graph. Improve runtime latency, perception, skills extraction from demos, action planning for live browser use, and automated improvement pipelines that learn from suboptimal actions.

Build and own full-stack product features for an AI-powered biopharma documentation platform. Develop React and TypeScript interfaces, backend services, APIs, data models, and production deployments. Design scalable systems for complex enterprise workflows, create AI-native scientific and regulatory content experiences, collaborate cross-functionally, and make pragmatic architectural decisions in a fast-paced startup environment.

Own and evolve ShiftKey's AI infrastructure: production retrieval/indexing, agentic platforms on AWS, graph-based multi-hop retrieval design, platform APIs, reference agents, and cost/observability guardrails for regulated (HIPAA) workflows.

The Robotics & AI Systems Development Engineer II develops robotic systems and AI solutions for manufacturing, focusing on autonomy, fleet management, and system integration.

Lead AI/ML safety engineering for Level 4 autonomous vehicles. Define SOTIF and functional safety strategies, derive requirements for learned systems, design runtime safety monitors and OOD detectors, assess dataset coverage and bias, analyze ML failure modes, and build evidence-backed safety cases. The role requires collaboration across perception, prediction, planning, systems, and safety engineering teams, with onsite work five days per week in Santa Clara, California.

Configure and operate an enterprise AI control plane covering MCP and LLM gateways, governance, access controls, observability, agent registries, and lifecycle management. Onboard internal and vendor MCP servers, implement authentication and policy patterns, build usage dashboards, troubleshoot production platforms, and create standards, runbooks, and onboarding documentation. The role partners with business, security, architecture, platform, and application teams to enable secure, governed AI agent integrations.

Develop and operate Java 21+ microservices using Spring Boot, REST APIs, event-driven integrations, relational databases, Docker, Kubernetes, and Azure. Modernize legacy monoliths, build automated tests, contribute to CI/CD and infrastructure automation, monitor production systems, and support incident response. Use Claude Code and other AI coding agents throughout development while evaluating generated code for security, reliability, maintainability, performance, and coverage. Own service architecture and evolution while collaborating with product, platform, security, QA, and business teams.

Leads the design, architecture, and delivery of AI-enabled applications and digital initiatives. Evaluates generative AI, machine learning, robotic process automation, intelligent data processing, and predictive analytics use cases. Manages project scope, budgets, schedules, resources, quality, SLAs, and stakeholders. Translates business needs into technical solutions, drives operational excellence, and mentors teams on emerging AI technologies across financial services.

Build and deploy production AI at the edge: translate operational problems into AI requirements, design and optimize models (vision, language, multimodal, time-series), deploy containerized workloads across cloud and disconnected edge, implement monitoring and retraining pipelines, and collaborate with customers and engineering teams to move systems from prototype to resilient production.

Lead AI agent optimization efforts by designing systems that improve token efficiency, response time, reliability, and output quality. Build agentic coding systems, develop AI application patterns, own features end to end, collaborate with engineering and field teams, support the developer community, and create public technical documentation. Work across Python, TypeScript, Java, and Go while serving as a domain expert in agent design and optimization.

Design, develop, and deploy scalable Agentic AI and multi-agent systems. Build microservices, RESTful APIs, cloud-native applications, and integrations with enterprise and distributed systems. Manage the full software development lifecycle, write tested and maintainable code, optimize performance, and evaluate emerging AI frameworks. Collaborate with engineering, architecture, product, and mission teams in Agile/Scrum environments while supporting continuous improvement and system sustainment.

Architect and build AI-driven workflow orchestration for autonomous cybersecurity operations. Responsibilities include automated incident response, LLM-based agent systems, event-driven Kafka pipelines, threat intelligence enrichment, multimodal data integration, real-time processing, and AI platform optimization using AWS Bedrock and Azure OpenAI. The role requires reliable, deterministic, auditable automation integrating security data, APIs, databases, and workflow systems.

Build and scale an enterprise Retrieval-Augmented Generation platform using Go and Python. Develop full-stack features, backend microservices, frontend interfaces, and AI/ML integrations in partnership with NVIDIA engineering teams. Own features from concept through production, while improving system performance, testing, security, availability, and reliability. Apply hands-on expertise in RAG, LLM pipelines, and AI-driven data retrieval architectures to deliver low-latency enterprise capabilities.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions, including LLM applications, multi-agent systems, agentic workflows, and enterprise automation. Own the AI solution lifecycle in cloud environments, influence architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders to deliver enterprise-scale solutions.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions for enterprise corporate functions. Build LLM applications, multi-agent systems, agentic workflows, RAG solutions, and automation using cloud AI platforms. Own the full AI solution lifecycle, influence architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions for enterprise corporate functions. Build LLM orchestration, RAG applications, agentic workflows, multi-agent systems, and automation using cloud AI platforms. Own the full AI solution lifecycle, establish architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders.

Lead architecture and implementation of AI-first backend and orchestration systems for Audit Suite. Drive scalable, reliable AI workflows (LLM integration, retrieval, vector stores), mentor engineers, set technical direction, and collaborate across product, design, and ML to deliver auditable, high-throughput document-centric solutions.

Build and own production AI-powered products across the frontend, backend, data, and cloud stack. Design multi-agent systems and RAG pipelines, orchestrate AI workflows, and deploy reliable, scalable features. Responsibilities include model serving, latency and cost optimization, prompt engineering, model evaluation, cloud infrastructure, and end-to-end product development using React, Node.js, PostgreSQL, and major cloud platforms.

Design, build, and deploy production-grade AI/ML and LLM solutions for claims processing, eligibility, prior authorization, and payment integrity. Build scalable data pipelines, integrate models with core payer systems, ensure HIPAA/PHI-safe pipelines and Responsible AI governance, monitor and retrain models, and partner with cross-functional teams to deliver reliable, CI/CD-driven solutions.

Leads technical architecture and solution design for complex cloud, AI, data center, hyperscale, and WAN networking sales opportunities. Translates customer requirements into scalable solutions, leads demos and proof-of-concepts, prepares RFP responses, and engages senior stakeholders. The role develops architecture playbooks, mentors sales engineering teams, influences deal strategy, and partners with customers, hyperscalers, service providers, and technology partners globally. Travel of up to 50%, including international travel, is required.

Implements and evaluates machine learning components under senior guidance, supporting data preparation, feature engineering, NLP/NLU experiments, retrieval and LLM pipelines, and model evaluation. Contributes to prototype and production AI services using Python and ML frameworks. Learns deployment, containerization, API, monitoring, and model lifecycle practices while participating in code reviews, sprint planning, documentation, and collaborative software development.