Applied AI ML Director, AI Agents and Agentic Systems

What you'll do

• Architect and implement core Agent SDK capabilities and reference implementations using production-ready code.
• Build specialized agents and reusable components to improve reliability, observability, and evaluation quality.
• Translate emerging generative AI techniques into scalable platform features for broad enterprise adoption.
• Design and implement evaluation frameworks for agent behavior, including quality, robustness, latency, and cost.
• Develop and optimize model-serving and workflow patterns such as orchestration, tool use, and planning.
• Drive technical decisions by identifying trade-offs and producing clear recommendations to resolve ambiguity.
• Improve platform performance and efficiency through profiling, bottleneck analysis, and system-level optimization.

What we're looking for

• Formal training or certification in applied artificial intelligence and machine learning concepts.
• 10+ years of experience in applied artificial intelligence and machine learning.
• 10+ years of hands-on experience building large-scale machine learning systems and platform services used by multiple teams.
• Strong software engineering skills for end-to-end delivery from design through implementation, testing, and operation.
• Extensive experience with machine learning frameworks such as PyTorch or TensorFlow.
• Hands-on experience with agentic and generative AI system design, including tool use, planning patterns, RAG, and evaluation methods.
• Strong experience with cloud and Kubernetes ecosystems for building and operating production workloads.
• Background in high-performance machine learning systems, including hardware acceleration considerations like GPU optimization.
• Experience contributing to or optimizing open-source machine learning frameworks or platform tooling (preferred).
• Experience building ML systems for production-grade AI workloads, including GenAI and agentic solutions (preferred).
• Experience with various open source ML/AI and agentic frameworks, LLM training frameworks, or other ecosystem tools (preferred).
• Advanced degree in Computer Science, Machine Learning, or a related field (preferred).
• Experience establishing applied science and engineering standards for responsible and reliable AI systems (preferred).