Top AI Engineer Jobs

Define low-level technical requirements and validate AI-enabled features for an enterprise application. Work with customers, systems engineers, developers, and stakeholders to convert requirements into Jira user stories/epics, perform functional testing, and advise on AI approaches and integrations. Engage in Agile delivery and cloud-based deployments (AWS).

Design, develop, and deploy production-grade AI/ML systems (NLP/LLMs, CV, generative models). Build scalable data pipelines, microservices/APIs, and cloud/edge deployments; optimize, version, and evaluate models; integrate solutions into classified operational environments and produce technical documentation.

Hybrid technical/customer-facing role building and refining AI risk-assessment modules using LLMs, writing SQL, and creating Power BI dashboards. Lead client onboarding, translate safety programs into the platform, partner on scoping and SOWs, serve as primary technical contact, QA AI outputs, and drive product improvements from client feedback.

Lead full-stack development and architecture of a SaaS platform, design and implement AI agent integrations, mentor engineers, own system and DB design (including PostGIS), ensure CI/CD, observability, security, and scalable cloud deployments.

Build end-to-end agent-powered tools and full-stack features using Typescript/Python. Integrate LLMs, vector databases, and agentic frameworks, develop reliable backend services, work with distributed systems and cloud architectures, and ship production solutions in tight experimentation loops with cross-functional partners.

The Applied AI Software Engineer will evaluate LLM-based agents, manage benchmarking, and ensure performance, safety, and reliability in healthcare settings. Responsibilities include designing evaluation plans, running experiments, collaborating with teams, and developing internal tooling for AI features.

The job involves developing machine learning algorithms, designing data pipelines, utilizing cloud infrastructure for ML solutions, and ensuring model governance practices.

Join Ello as an AI/ML Engineer to create AI-driven educational products, enhance learning experiences, and analyze educational outcomes through machine learning.

Senior engineer responsible for designing, deploying, and operating the AI-enabled digital workplace. Own identity (Entra ID), endpoint (Intune/Jamf), and M365 services; integrate and govern AI tools (Copilot, assistants); drive IT automation, service excellence, incident/problem management, AI governance, and enablement across IT.

Support planning, design, and implementation of a mission-critical system. Elicit and document functional and technical requirements, perform workflow/business process engineering, and act as liaison between customers and developers. Collaborate with Product Owner, Task Lead, Scrum Master, system engineers, and developers to facilitate sprint execution, functional testing, demos, and stakeholder communication.

Build, productionize, and operate AI-driven features and integrations across frontend, backend, and model layers. Prototype and deliver agent-driven experiences (conversational interfaces, RAG, tool-calling), integrate LLMs and external data sources, and collaborate with product and design. Participate in on-call/standby duties.

Optimize large-scale distributed ML training and petabyte-scale offline inference for throughput and cost. Profile across stack, close performance gaps, improve multi-node scaling and cluster goodput, and build benchmarking and observability tooling while collaborating across teams.

Design, build, and operate scalable backend services and internal APIs for an AI platform. Integrate LLMs and AI tools into production workflows, ensure platform reliability via observability and incident response, optimize AI runtime performance and cost, and contribute to shared infra like CI/CD and deployments.

Own end-to-end physical implementation of AI accelerator and RISC-V IP blocks from synthesis through PnR, timing closure, and GDSII signoff. Drive PPA outcomes, collaborate with RTL owners, build floorplans, power grids, pin placement and clocking strategies, and deliver LEF/DEF, timing and power models, GDS, and signoff reports for tapeouts on advanced nodes.

Lead architecture and hands-on delivery of AI-enabled, cloud-native enterprise software. Define AI-native engineering standards, implement CI/CD and automated testing, ensure security, observability, and operational excellence, mentor engineering teams, and drive adoption of modern Azure and GitHub development ecosystems.

The Principal AI/ML Engineer will lead the development of AI/ML platforms, drive technology architecture, ensure quality and integration, and provide strategic leadership while managing a team of engineers.

First Sales Engineer responsible for technical pre- and post-sales with enterprise finance customers. Design and ship agentic AI solutions, tailor proofs-of-concept to AR workflows and ERP integrations, and drive implementations to production. Act as technical SME to accelerate sales and product adoption.

Lead architecture and hands-on development of cloud-native AI web applications. Design end-to-end solutions integrating LLMs, vector search/RAG, real-time data, and scalable backend services. Implement model serving, RAG pipelines, AI agents, and ensure quality via testing and CI/CD while leading cross-functional engineering teams and translating business requirements into production-ready systems.

Lead execution of Product Engineering AI and automation initiatives across photonics products. Build and deploy reusable AI/automation tools, transform engineering data into AI-ready systems, and drive cross-functional adoption to improve development, qualification, manufacturing, and reliability processes.

Build and enhance LangSmith frontend features: implement user-facing UI with React and TypeScript, create reusable components, translate designs into high-quality code, optimize performance, and collaborate with backend engineers and designers to improve observability and evaluation workflows for LLM applications.

Develop, debug, and maintain AI applications and tools for large-scale data analysis. Collaborate with engineers and leadership to design solutions, document procedures, and support users.

Design, fine-tune, and deploy multimodal vision-language and VLA models for autonomous eVTOL aircraft; benchmark, validate in simulation/hardware/flight, and productionize models for edge deployment under safety and performance constraints.

Lead AI system safety assurance for humanoid robots: perform STPA/FMEA hazard analyses, assess model-update risks, define AI safety requirements (inputs, confidence, OOD), develop V&V test cases, deploy validation cells, maintain traceability, and produce evidence packages for certification.

Design, fine-tune, and deploy LLMs and RAG pipelines in private, secure environments. Build multi-agent systems, private inference infrastructure, evaluation frameworks, and observability tooling. Collaborate with platform teams to ensure secure, scalable production AI for enterprise customers.

Develop, train, evaluate, and deploy spatial generative AI models for home renovation and design. Define inference framing, prepare image-based datasets with labeling partners, iterate with user feedback, and deploy performant models into production architecture.