Job Title: Principal GCP Data & AI/ML Engineer / Architect
Experience Level: 10+ Years
Location: Remote (US-Based)
Position Overview
This role requires an expert who is equally skilled at executing complex Enterprise Data Warehouse (EDW) migrations to BigQuery and taking complete technical ownership of the core machine learning lifecycle—including hands-on model building, distributed training, optimization, and production deployment.
Key Responsibilities
Hands-on Model Development: Architect, build, and evaluate both traditional machine learning models (e.g., regression, classification, clustering, time-series forecasting) and deep learning architectures customized for specific business use cases.
Scalable Training & Fine-Tuning: Establish distributed training workflows on GCP to handle massive datasets efficiently. Optimize training costs and performance, and lead the fine-tuning of foundational LLMs using specialized internal datasets.
Production Deployment & MLOps: Package and deploy models as low-latency REST endpoints or high-throughput batch prediction jobs on Vertex AI, GKE, or Cloud Run. Implement automated CI/CD for ML (MLOps) to manage data versioning, model registries, and automated retraining loops.
Data Pipeline Engineering: Build robust, clean upstream data and feature engineering pipelines using Cloud Dataflow (Apache Beam), Dataproc (Spark), and Cloud Composer (Airflow) to feed training environments and live inference engines.
Model Governance & Monitoring: Set up comprehensive tracking for model drift, data drift, and performance degradation post-deployment, ensuring continuous compliance, accuracy, and enterprise-grade security.
Required Skills & Experience
Experience Floor: Minimum 10+ years of professional experience across data warehousing, data engineering, and applied data science, with a proven track record of shipping models to production.
Core AI/ML Mastery: Strong practical experience across the entire model lifecycle: building frameworks from scratch (Python, TensorFlow, PyTorch, Scikit-Learn), configuring training jobs, tuning hyperparameters, and operationalizing endpoints.
GCP AI Stack: Deep production expertise with Vertex AI (including Vertex AI Pipelines, Model Registry, Feature Store, and Vertex AI Workbench).

Data Stack & Automation: Highly proficient in Python, SQL, and PySpark. Solid understanding of Infrastructure as Code using Terraform to manage ML infrastructure securely.
Preferred Qualifications
Google Cloud Certified Professional Machine Learning Engineer
Practical experience implementing Retrieval-Augmented Generation (RAG) patterns or deploying generative AI agents.

Thanks
Nithya
[email protected]

Principal GCP Data & AI/ML Engineer / Architect

Similar Engineering Jobs

Lead Data Engineer

Senior Data Engineer

Senior Mechanical Engineer - Data Centers

Data Modeling Engineer

Machine Learning Engineer, Data Mining

Engineering Process and Work Management Analyst