Top AI Engineer Jobs

Lead technical POCs and pre-sales engagements to turn proofs-of-concept into production deployments. Troubleshoot complex integrations (identity, security, networking, observability), design custom solutions, run onboarding and enablement, and act as the technical lead during sales to ensure successful rollout and adoption.

Embed with client teams to design, build, and productionize AI-driven workflows and automations. Own workstreams from prototype to reliable production: prompt design, retrieval, model selection, integrations, error handling, monitoring, and documentation to drive measurable operational impact and reusable platform patterns.

Design and build backend systems and internal tooling for AI-native products: secure data access for agents, agent orchestration, evaluation/observability for non-deterministic outputs, performance and latency optimization, and end-to-end feature ownership from concept to production.

Lead architecture, build, and deploy enterprise-grade Agentic and Generative AI multi-agent solutions. Develop RAG pipelines, vector search, and AI microservices in Python (FastAPI/Flask). Deploy on cloud (AWS/Azure/GCP) with Docker/Kubernetes and CI/CD, implement AI security/governance, mentor engineers, and drive AI strategy and platform roadmaps.

Lead architecture and delivery of frontier AI solutions: define end-to-end technical designs, establish reusable AI engineering patterns, guide platform integration and production readiness, advise cross-functional stakeholders, and ensure secure, scalable, observable AI products that are commercially viable.

Evaluate AI-generated coding interactions end-to-end, judging usefulness, high-level correctness, engineering judgment, explanations, and reasoning. Provide clear, opinionated feedback, distinguish quality levels, and help define standards for excellent AI-assisted developer interactions.

Design, develop, and upstream-enable AI/HPC communication middleware (MPI/SHMEM, NCCL/RCCL). Integrate middleware with transports (libfabric/UCX/verbs), optimize performance, collaborate across kernel/driver and switch teams, analyze performance traces, reproduce and fix customer issues, and contribute to open-source projects.

Develop and optimize AI/HPC communication middleware (MPI/SHMEM, CCL/NCCL/RCCL) over Cornelis interconnects; integrate with transports (libfabric/OFI, UCX), contribute upstream open-source, collaborate across kernel/driver and switch teams, and analyze performance traces to reproduce and fix customer issues.

Build and maintain backend services and infrastructure for ML and generative AI features. Develop scalable, resilient, high-performance Python services (async frameworks, streaming), deploy to Kubernetes, integrate RAG and LLM orchestration, instrument LLM observability, and collaborate cross-functionally to deliver production-ready AI products.

Lead development and adoption of AI-driven and traditional test automation, maintain UI automation frameworks, own performance/load testing strategy, align testing to architecture and risk, mentor QA engineers, and collaborate with product and engineering to improve quality and testing efficiency.

Lead design, build, and deployment of AI-enabled analytics using Microsoft AI technologies and cloud data platforms. Develop AI assistants, copilots, RAG and agentic workflows, integrate solutions with Power BI and semantic models, prototype and productionize with Python and SQL, implement governance and observability, and partner with BI, data engineering, and business stakeholders to scale enterprise AI use cases.

Build end-to-end product features for an AI-native assistant: design polished UI/UX, implement backend plumbing, and ship LLM-powered experiences across web, desktop, and mobile. Work on suggestion engines, workflow builders, team-shared automation surfaces, and trusted agent interactions. Be a foundational full‑stack hire influencing product direction and delivering a fast, delightful experience.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Lead end-to-end development and production deployment of agentic AI systems and full-stack products. Architect integrations with property management systems, improve reliability and observability, define engineering standards, and partner with product and operations to ship scalable AI agents across voice, text, and email.

Build and ship full‑stack, LLM-powered product features (tutoring agents, copilots, dashboards, retrieval and evals). Own smaller systems end‑to‑end, use AI coding tools daily, write evals and regression tests, and iterate rapidly with founders and senior engineers.

The AI Application Engineer will design and develop applications using Large Language Models, implementing workflows, ensuring code quality, collaborating with teams, and deploying solutions on cloud platforms.

The AI Enablement Engineer will integrate AI tools into engineering workflows, enhance productivity, and ensure compliance with data security standards while supporting engineers' adoption of AI solutions.

Lead architecture and delivery of enterprise AI agent solutions on the Moveworks/ServiceNow platform, integrating with enterprise systems. Build and tune AI workflows, partner with product and vendors, and provide L2/L3 IT operations support (servers, networks, virtualization, cloud). Act as help desk escalation, troubleshoot complex issues, and continuously improve IT processes.

The AI Engineer will develop scalable AI systems, automate workflows, and collaborate with business teams to enhance operational efficiency across OpenSesame.

As a Generative AI & Machine Learning Engineer, you'll manage AI/ML product development, support model lifecycle, and ensure solutions meet customer needs, fostering collaboration and innovation.

The Senior Software Engineer will build AI-driven backend systems for accounting, develop robust services, and collaborate with cross-functional teams.

In this role, you will lead the AI engineering lifecycle for Snowflake Database Engineering products, enhancing workflows and collaborating to address customer needs.

As a Forward Deployed Engineer, you'll partner with pharma and biotech teams to convert scientific workflows into AI-powered systems, ensuring accuracy and reliability in implementation. You'll oversee customer accounts, drive the implementation lifecycle, and collaborate with product and engineering teams to optimize AI applications.

As a Founding Engineer, you will build core AI systems for software quality assurance, design automation components, and define architecture alongside the founders.

As a Founding Product Engineer at Spur, you'll design, code, test, and deploy end-to-end features, gathering user feedback to drive product improvements and iterating quickly across teams.