Principal Engineer – AI Model Training & Data Strategy

About the role

Maxonic maintains a close and long-term relationship with our direct client. In support of their needs, we are looking for:

Job Title: Principal Engineer – AI Model Training & Data Strategy

Location: Remote

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)

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

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

About Maxonic:

Since 2002 Maxonic has been at the forefront of connecting candidate strengths to client challenges. Our award winning, dedicated team of recruiting professionals are specialized by technology, are great listeners, and will seek to find a position that meets the long-term career needs of our candidates. We take pride in the over 10,000 candidates that we have placed, and the repeat business that we earn from our satisfied clients.

Interested in Applying?

We’d love to hear from you! Please click apply with your most current resume and anything else you’d like us to know about you – commute preferences, desired work environments, etc. You should also feel free to call Saurav at 408-400-2332 or email me at saurav@maxonic.com