Principal AI Engineer

**Job Description:** **Job Summary** 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 - Deep experience with parameter-efficient fine-tuning (QLoRA, LoRA, PEFT), quantization, and the tradeoffs versus full fine-tuning - Proficiency with ML/DL frameworks and libraries (e.g., PyTorch, Hugging Face Transformers/PEFT/TRL, scikit-learn) - Experience building and operating large-scale data pipelines and platforms (e.g., Spark, Ray, dbt, or equivalents) - Strong grasp of data management: dataset storage architecture, versioning, lineage, governance, and PII handling - Experience with experiment tracking and reproducible ML (e.g., MLflow, Weights & Biases) - Understanding of distributed training and GPU/compute optimization - Ability to define strategy and standards while remaining hands-on in code - Strong stakeholder collaboration and problem-solving skills **Education and Experience** - Bachelor’s degree in Computer Science, Engineering, or related discipline; advanced degree in ML, AI, or Data Science preferred - 12 or more years of experience in AI/ML engineering, applied ML, or data engineering, with significant hands-on model training and fine-tuning **Disclaimer** The above statements describe the general nature and level of work performed in this role. Other duties may be assigned. **Pay Transparency Statement:** US Base Salary Range: $159,600.00 - $207,500.00 Base pay offered to new hires may vary based upon factors including relevant industry and job-related skills and experience, geographic location, and business needs.\* The range displayed does not encompass the full potential of the role, which allows for further growth and career progression. In addition, as a part of our total compensation package, this role may be eligible for the Vertex Bonus Plan (VOB), a role-specific sales commission/bonus, and/or equity grants. Learn more about [Life…