Principal AI Engineer
Tasks
- Build reproducible training pipelines and experiment tracking
- Curate and clean training and validation datasets
- Define evaluation methodology and benchmarks
- Define model training strategy across AI and large language models
- Define training data storage versioning and access standards
- Design and optimize data pipelines for ingestion cleaning labeling and feature engineering
- Establish data governance lineage quality licensing and PII handling
- Fine-tune large language models
- Generate synthetic data
- Mentor engineers and improve model training and data quality
- Operationalize trained models for production
- Optimize training cost and compute utilization
- Train evaluate and tune traditional AI ML models
Perks/Benefits
- N/A
Skills/Tech-stack
DBT | Data Governance | Data Lineage | Data Quality | Dataset versioning | Distributed Training | GPU Optimization | Hugging Face | Hugging Face Transformers | LoRA | MLflow | PEFT | PII | PyTorch | Python | QLoRA | Quantization | Ray | Scikit-learn | Spark | TRL | Weights and Biases
Education
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