Applied AI ML Director - AI Agents and Agentic Systems
Salary not listed • Palo Alto, CA, United States • Full-time
Posted 1mo ago
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Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Agent Engineer
About the job
You will join a platform team shaping how agentic systems are built, evaluated, and deployed at scale. This is a deeply hands-on role where you will design core platform capabilities, write production-quality code, and partner across product and engineering to drive adoption. You will help turn advanced agent and generative AI techniques into reliable, reusable building blocks that teams can use to deliver measurable business outcomes. As an Applied Artificial Intelligence and Machine Learning Director, you will build and evolve an Agent software development kit (SDK) and specialized agent capabilities for broad enterprise use, owning key technical decisions, setting practical engineering standards, and delivering critical components end-to-end. You will collaborate with partners across data, platform, and application teams to ensure solutions are scalable, secure, and maintainable.
Responsibilities
- Architect and implement core Agent SDK capabilities and reference implementations with a strong emphasis on production-ready code.
- Build specialized agents and reusable agent components, improving reliability, observability, and evaluation quality.
- Translate emerging agentic and generative AI techniques into scalable platform features for adoption.
- Design and implement evaluation approaches for agent behavior, including quality, robustness, latency, and cost trade-offs.
- Develop and optimize model-serving and workflow patterns for agentic systems, including orchestration, harness, and tool use.
- Partner with product and engineering stakeholders to align platform capabilities with success metrics and prioritized outcomes.
- Drive technical decisions by clarifying ambiguity, identifying trade-offs, and producing crisp recommendations and designs.
- Improve the agentic platform’s performance, accuracy, and efficiency through profiling, bottleneck analysis, and system-level optimization.
Requirements
- Formal training or certification on applied artificial intelligence and machine learning concepts and 10+ years of applied experience.
- 10+ years of hands-on experience building large-scale machine learning systems and platform services used by multiple teams.
- Strong software engineering skills applied to real-life ML/AI areas, with end-to-end delivery ownership.
- 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, including building and operating production workloads.
- Background in high-performance machine learning systems, including hardware acceleration considerations (e.g., GPU optimization).
- Proven ability to influence across teams without formal authority through technical leadership, clear communication, and strong execution.