AI Engineering jobs

Join a strategic AI team building production-grade agents, assistants, and reusable AI skills for cybersecurity applications. The role focuses on modern AI application development rather than model training.

Join an industrial technology company developing semiconductor inspection and manufacturing solutions. Bridge research and production by integrating, optimizing, deploying, and monitoring machine learning models in high-throughput systems.

Lead the design and development of large-scale machine learning inference and data processing systems for production workloads. Collaborate with ML researchers and infrastructure teams to build scalable, efficient, and reliable solutions.

Lead the end-to-end design, development, optimization, and deployment of production-grade AI systems. Projects focus on either LLM-based agentic systems or computer vision, including real-time video analytics, depending on expertise and business needs.

Build, implement, and maintain AI-powered systems that automate knowledge collection, analysis, competitive intelligence, and internal knowledge sharing. Own projects from concept through delivery and adoption while partnering with information systems, IT, data science, and internal stakeholders.

Design and scale ML and LLMOps platforms that enable data scientists and security researchers to train, deploy, serve, and continuously improve advanced AI systems.

Lead the design, development, and production delivery of generative AI capabilities for a consumer relationship guidance platform. Own AI infrastructure, LLM integrations, prompt experimentation, and cross-team enablement.

Join a business-facing technology team developing complex data pipelines, AI/ML systems, and firmwide software platforms for systematic financial research and strategy deployment.

Own the production lifecycle of classical ML, LLM, and agentic AI solutions by building reusable deployment platforms and operating reliable model pipelines.

Design, prototype, and deliver agentic AI applications for complex scientific and operational workflows in pharmaceutical research and development. Apply large language models, retrieval, tool integration, and workflow orchestration to build reliable, evaluated solutions in partnership with science,