Top AI Engineer Jobs

Lead the AI development team to architect, build, deploy, and operate enterprise AI solutions on Azure (RAG, LLMs, agentic workflows). Hands-on coding (Python/FastAPI), enforce engineering standards, mentor engineers, and collaborate cross-functionally to ensure secure, scalable production systems.

Lead design and delivery of agentic AI systems and LLMOps on AWS to generate, validate, and deploy infrastructure-as-code (Terraform). Build multi-agent applications, RAG pipelines, and AIOps for cloud operations; integrate AI into CI/CD and ITSM. Set technical direction, establish guardrails/policy-as-code, curate reusable Terraform modules, and mentor engineering teams.

Design, train, test, and deploy computer vision and sensor-fusion models for real-time missile warning and target tracking. Optimize performance with GPU programming, contribute to embedded and fire control systems, and collaborate across multidisciplinary teams to deliver space-focused AI solutions.

Design and build production-grade agentic AI systems and RAG pipelines, partner with business leaders to automate complex workflows, prototype across AI platforms, integrate models, APIs and data sources, and drive enterprise adoption and scalable solutions.

Hands-on principal engineer building production AI software across frontend, backend, cloud, and AI systems. Design scalable services, debug distributed systems, improve reliability, evaluate new models and technologies, and partner with CTO to deliver high-impact platform features.

Build and ship full-stack AI solutions from discovery through production: prototype with LLMs and agent patterns, integrate models with data and systems, gather user feedback, iterate, and deliver secure, monitored internal tools.

Join as an early-career full stack engineer to build frontend and backend product features, integrate AI coding tools into development workflows, translate product requirements into implementations, and grow toward autonomous full stack delivery while improving cross-team processes.

Lead design and deployment of scalable ML platforms and tools, manage complex technical projects, mentor junior engineers, and collaborate across teams to enhance autonomous driving systems.

As a Senior AI/ML Engineer, you'll enhance developer productivity by leading CI solutions, mentoring engineers, and implementing scalable systems.

Enable production readiness and scale fab capacity by modeling impacts of tools and process changes, building automated AI-enabled planning models, maintaining capacity/utilization models, and delivering data-driven recommendations and executive presentations.

Lead design and delivery of enterprise AI/ML and intelligent automation platforms. Define architecture strategy, build backend APIs and event-driven pipelines, deploy cloud-native solutions, drive adoption of generative and agentic AI, mentor engineering teams, and ensure security, scalability, and governance across AI initiatives.

Embed with customers to learn and rebuild critical business functions, then design and ship production GenAI/LLM systems on AWS. Own delivery end-to-end: operator immersion, design, implementation, evaluation, and stakeholder alignment while remaining hands-on in code and cloud-native ops.

As a Senior AI Engineer at Knotch, you will develop AI agents for workflow automation, design backend and frontend services, and collaborate with teams to integrate AI into products. Your role includes optimizing systems for performance and ensuring agent safety and governance.

Build and maintain evaluation, monitoring, and CI systems to ensure AI quality, reliability, and compliance. Track metrics, detect drift, improve prompting, model selection, and pipelines, productionize robust AI workflows, support audits (SOC 2), and act as a quality gate for AI-related releases.

Design, develop, and deploy document AI and multimodal pipelines (OCR, LLM reasoning, extraction validation). Lead pre-sales technical engagements, build POCs/demos, support sales with technical value propositions, deploy solutions across cloud and enterprise VPCs, and collaborate with product and partners to drive customer adoption and roadmap feedback.

Provide SETA support to government teams building and evaluating AI-enabled applications. Advise on AI design, prototypes, integration, and technical risk. Assess LLMs, RAG, agentic workflows, orchestration frameworks, and evaluation harnesses. Review architectures, recommend engineering tradeoffs, develop prototypes and guidance, and support test and evaluation. Collaborate with platform engineers, product leads, and mission users to ensure operational relevance and maintain current knowledge of emerging AI tools and practices.

Provide technical advisory and hands-on AI engineering support for design, evaluation, and advancement of AI-enabled applications. Assess LLMs, RAG, agentic workflows, orchestration frameworks, and architectures; develop prototypes, guidance, and technical recommendations; support test and evaluation activities and brief government stakeholders while ensuring operational relevance.

Lead design and implementation of customer-facing AI features and foundational AI infrastructure (orchestration, retrieval, evals, context management). Identify high-leverage opportunities, create reusable AI primitives, set technical direction, and drive adoption through cross-functional collaboration and product-focused execution.

Architect and own core capabilities of an internal AI agent platform: design deployment patterns, shared services, observability, integrations, and developer tooling; partner with stakeholders to turn strategic goals into production-ready agent solutions; troubleshoot and unblock builders while improving platform reliability, scalability, security, and reuse.

Lead and mentor a San Diego engineering team while staying hands-on building production LLM systems (RAG, agents, document processing). Drive architecture, evaluate models and tooling, own end-to-end delivery, collaborate with clients, and create reusable internal tooling and best practices to improve team performance and client outcomes.

Lead identification, scoping, and delivery of production AI features and automation. Prototype and ship LLM-powered solutions, build RAG and agentic pipelines, create low-code workflows, integrate AI into existing systems, and partner cross-functionally to prioritize, deploy, monitor, and iterate on AI initiatives across product, operations, and business teams.

Lead design and delivery of agentic AI-powered full-stack features. Ship production-ready systems using React, Next.js, TypeScript, and PostgreSQL. Architect scalable solutions, prototype and evaluate agentic features, perform rigorous code review (including AI-generated code), and collaborate with product and engineering teams to iterate rapidly across multiple concurrent projects.

Lead customer-facing, end-to-end AI engagements: design and ship agentic AI systems, LLM applications, and production ML pipelines; build scalable backend services and integrations; implement guardrails and monitoring; coordinate cross-functional delivery; mentor junior engineers; and represent H2O.ai in technical briefings and pre-sales activities.

Lead end-to-end technical engagements to design, build, and deploy agentic AI and LLM-powered applications (RAG, fine-tuning, multi-agent systems). Own full ML lifecycle, backend services, integrations across cloud/on‑prem, and production LLMOps. Coordinate cross-functional teams, mentor junior engineers, and represent H2O.ai in customer workshops and executive briefings to deliver production-grade, compliant AI solutions.

Design, build, deploy, and operate production-grade AI agents and RAG solutions. Lead end-to-end ALM: design, testing, CI/CD, security, and customer enablement. Deliver training, workshops, and on-site support while ensuring governance, performance, and adoption.