Top AI Engineer Jobs

Responsible for the full lifecycle of AI/machine learning on resource-constrained hardware, including data ingestion, model development, and deployment on embedded devices, while working closely with cross-functional teams.

The Sr. Software Engineer will lead AI product development, integrate AI solutions across workflows, and mentor team members while ensuring system scalability and resilience.

Lead AI software resiliency development, implementing features for reliability and fault tolerance in large-scale AI supercomputers, requiring hands-on coding and collaboration.

Lead the design and development of AI solutions and MCP servers, focusing on AWS infrastructure and Python development. Responsibilities include automating processes, managing deployments, and conducting architectural governance.

As an AI Algorithm Developer, you'll design and implement machine learning solutions for semiconductor processing, focusing on predictive models and data analysis.

Design and develop core platform features for synthetic data generation, LLM operations, and agentic workflows. Oversee API design and scalable compute services, enhancing AI model performance for enterprise clients.

As a Senior Software Engineer, you'll build AI features focused on LLMs and RAG systems, collaborating across teams to ensure robust performance.

Fine-tune and optimize LLMs (including multi-modal) for humanoid robots and autonomous vehicles; build scalable training and deployment pipelines; work on model efficiency (quantization, weight sharing), synthetic data generation, and cross-team product alignment.

Lead and mentor teams in developing language model-based AI solutions, ensuring high technical standards, quality implementations, and effective communication with stakeholders.

As a Senior Formal Verification Engineer, you will develop verification strategies and environments for AI SoCs, ensuring the correctness of digital designs through formal verification methods and collaboration with engineering teams.

The AI Solutions Engineer will implement AI innovations across ERP systems, configure AI capabilities, and develop custom AI solutions for HR, finance, and procurement, utilizing cloud engineering expertise.

The Principal Software Engineer will design AI-assisted developer tools, improve software development workflows, and integrate new technologies for government contracts.

Lead planning, design, development, and testing of AI products; partner with stakeholders to translate business needs; mentor and enhance development capabilities.

Design, prototype, and productionize Generative AI and LLM features across backend and frontend. Integrate ML models, fine-tune, perform prompt engineering, operate inference platforms, and support AI-based product launches while participating in on-call duties.

Lead the development of AI compiler tools, enhance performance analysis, collaborate with hardware teams, and mentor engineers to improve compiler tooling.

The AI Marketing Ops Consultant builds AI-powered marketing automation and workflows, integrates tools, documents processes, and collaborates with teams to optimize marketing operations.

The Principal Machine Learning Engineer leads the deployment of multi-modal AI models on mobile platforms, setting technical vision and optimizing inference performance, while mentoring a team and collaborating cross-functionally.

Lead engineering of the full embodied systems stack for real-world robots, owning onboard inference, data collection, teleoperation, and on-robot RL systems. Set architecture and technical direction, deliver reliable real-time deployments, close the loop between data, models and actions, and mentor engineers to scale embodied AI in industrial environments.

Lead and manage SAP ERP and procurement systems while developing AI-powered analytics, agents, and modern dashboards. Ensure integrations, data integrity, and adoption of global ERP initiatives. Build Python-based applications, implement RAG solutions, develop SQL data pipelines and REST APIs, and partner with stakeholders to deliver scalable automation and reporting.

Design, develop, and deploy end-to-end GenAI/LLM solutions on AWS. Build cloud-native, containerized systems (ECS/EKS/Docker/Kubernetes), support production geospatial applications, ensure security/compliance, and deliver production-ready architectures in collaboration with clients and teams.

Implement, administer, and support Microsoft 365, Purview, and Copilot services, focusing on compliance (DLP, sensitivity labels, eDiscovery, audit, insider risk), Copilot/AI administration, incident and problem management, operational resilience, and collaboration with security, architects, and business stakeholders.

Lead architecture and build AI-native developer tooling and autonomous agent systems to accelerate engineering workflows. Drive platform standards for reliability, safety, observability, and cost-efficiency while partnering across teams and mentoring senior engineers.

Lead design and implementation of AI-first testing frameworks and automation to validate LLM integrations and agentic Product Led Growth workflows. Perform white-box testing, identify and fix quality gaps, implement human-in-the-loop model evaluation, and collaborate cross-functionally to integrate quality across the agile delivery lifecycle.

Design, build, and optimize Azure cloud-native applications using .NET/C#. Implement serverless, distributed architectures, data pipelines, and CI/CD. Leverage AI-assisted development tools, apply TDD and automated testing, mentor engineers, participate in architecture and DevOps, and ensure performance, security, and compliance.

Lead design and build of AI-powered observability and AIOps platforms: agentic AI agents, predictive insights, automated remediation, integrations with observability tools, developer-first tooling, cloud-native scalable services, and end-to-end automation across hybrid environments. Serve as technical lead, define enterprise architecture, drive CI/CD and IaC practices, mentor engineers, and collaborate with cross-functional teams to improve reliability and incident response.