Principal AI Engineer

The Principal Engineer, AI Model Training & Data Strategy owns how Commercial AI (CAI) products train, fine-tune, and evaluate models, and how the data behind those models is sourced, curated, stored, and governed. This is primarily a model-training role with a strong secondary focus on the data management and pipelines that make high-quality training possible. The role defines the enterprise training strategy and the standards for how and where training data from Commercial AI products is stored, versioned, and reused.

Essential Job Functions and Responsibilities

- Define and own the end-to-end model training strategy across CAI products, spanning traditional AI/ML models and large language models

- Fine-tune large language models using parameter-efficient techniques (e.g., QLoRA, LoRA, PEFT) and full fine-tuning where warranted

- Train, evaluate, and tune traditional AI/ML models (classification, regression, ranking, clustering, and similar)

- Work with large volumes of data – design and optimize pipelines for ingestion, cleaning, labeling, and feature engineering

- Define standards for how and where training data from Commercial AI products is stored, versioned, and accessed (data lakes/warehouses, feature stores, dataset registries)

- Establish data governance, lineage, quality, licensing/consent, and PII-handling practices for training data

- Build reproducible training pipelines and experiment tracking (datasets, hyperparameters, checkpoints, and metrics)

- Define evaluation methodology and benchmarks for model quality, including offline evaluation and regression testing

- Curate and clean training, validation, and test datasets, including synthetic data generation where appropriate

- Optimize training cost and compute utilization (GPU efficiency, distributed training, quantization)

- Partner with product and platform teams to operationalize and hand off trained and fine-tuned models to production

- Mentor engineers and raise model-training and data-quality maturity across teams

Knowledge, Skills, and Abilities

- Strong hands-on experience training and fine-tuning both traditional AI/ML models and LLMs in production