Principal Architect – AI Engineering

Location: San Francisco Bay Area, US

Sector: Enterprise AI

Compensation: Competitive Package

We are partnering with a rapidly growing enterprise technology platform that is redefining how AI drives business intelligence and decision-making. The organization leverages cutting-edge machine learning, conversational AI, and automation to deliver high-impact solutions to complex enterprise challenges.

They are looking for a Principal Architect to shape the next generation of AI capabilities. The successful candidate will define the technical vision for model training, evaluation, and deployment at scale, while guiding a talented team of ML and data engineers. Your work will influence how advanced AI powers real-world enterprise solutions.

Key Responsibilities:

• Define the architecture for model training, evaluation, and deployment across the AI platform.
• Evaluate and select model approaches (open-weight, commercial, hybrid) against enterprise accuracy and cost requirements.
• Design evaluation frameworks that measure model quality across multiple task categories.
• Architect training data pipelines, including synthetic data generation and quality validation.
• Lead design of retrieval-augmented generation (RAG) systems integrating structured knowledge retrieval.
• Establish technical standards for model safety, tenant data isolation, and responsible AI.
• Mentor and guide ML and data engineers across multiple locations, ensuring delivery excellence.
• Collaborate closely with product, AI platform, and cloud operations teams.
• Hands-on coding, prototyping, and reviewing early-phase approaches.

The Ideal Candidate:

• Proven experience in software engineering, focused on ML/AI systems.
• Proven experience training, fine-tuning, and deploying large language models to production.
• Deep expertise in transformer architectures, training optimization techniques (LoRA, QLoRA, PEFT, RLHF, DPO), and inference serving.
• Proficiency in Python, PyTorch, and ML infrastructure tooling.
• Advanced degree in Computer Science, Machine Learning, or equivalent practical experience.
• Experience with enterprise B2B SaaS platforms is a strong advantage.