Principal Machine Learning Engineer
Core
Lead the technical direction of GenAI and agentic ML systems powering enterprise-grade AI agents, including reasoning, retrieval, tool use, and integrations across SaaS products.
Role type
Principal Machine Learning Engineer (Agentic AI/GenAI)
Builds
Scalable production pipelines for model training, fine-tuning, retrieval (RAG), agent orchestration, and evaluation; multi-year ML roadmap for GenAI infrastructure.
Domain
Enterprise Agentic AI, Generative AI, Large Language Models (LLMs)
Deliverable
production ML models
Required skills
Large-scale ML system architecture, Knowledge retrieval and Search, Agentic Systems and Frameworks, Deep learning, LLMs, Distributed training and inference optimization, Python, PyTensor/PyTorch, Model lifecycle management (training, serving, monitoring), System scalability design
Preferred skills
PhD in CS/ML/Statistics, Experience with MCP, browser, and vision pipelines, Bias-aware development practices
Technologies
TensorFlow, PyTorch, Python, C++, Java
Responsibilities
Lead technical direction of GenAI and agentic ML systems; Architect scalable production pipelines for model training, fine-tuning, retrieval, and agent orchestration; Define multi-year ML roadmap for GenAI infrastructure; Integrate cutting-edge ML methods and research into products; Optimize trade-offs between accuracy, latency, cost, and reliability; Champion engineering excellence (observability, reproducibility, testing); Mentor senior engineers and researchers; Collaborate cross-functionally to align ML innovation with enterprise needs; Influence data strategy for retrieval indices and embeddings; Drive system scalability for billions of knowledge objects.
Seniority
Principal, hands-on IC with org-building and strategy influence