Machine Learning Engineer

About the Role

Volga Infotec is seeking a dedicated Machine Learning Engineer specializing in Data and Model Validation. This crucial role ensures the integrity, reliability, and production-readiness of our advanced machine learning systems. You will be instrumental in overseeing data quality, rigorously evaluating ML models, detecting drift in real-time, and performing release validation for large-scale ML pipelines. This is an engineering-focused position, demanding ownership of ML quality rather than traditional QA tasks.

Key Responsibilities

• Own the end-to-end data validation and quality checks for ML pipelines, covering both training and inference stages.
• Establish and implement baseline expectations for datasets and features.
• Validate ML models using industry-standard metrics such as F1, precision, recall, and AUC.
• Conduct regression testing throughout model retraining cycles to ensure consistent performance.
• Proactively detect and address data drift, feature drift, and prediction drift.
• Develop and implement automated quality gates to manage model release progression.
• Seamlessly integrate validation checks into CI/CD and MLOps workflows.
• Validate the performance and accuracy of both batch and real-time inference systems.
• Define clear criteria for model release readiness.
• Meticulously document all validation logic and established monitoring thresholds.

Mandatory Skills

• Demonstrable hands-on experience with data validation frameworks like Great Expectations or Deequ.
• Proficiency in Python, including libraries such as Pandas, NumPy, and PyTest.
• A strong foundational understanding of machine learning principles.
• Proven experience in validating ML models deployed in production environments.
• Solid experience working with ML pipelines.
• Familiarity with CI/CD principles and implementation.

Nice-to-Have Skills

• Experience with popular ML libraries like Scikit-learn, TensorFlow, or PyTorch.
• Exposure to ML platforms such as MLflow, Azure ML, SageMaker, or Vertex AI.
• Experience with Large Language Models (LLMs) or Generative AI (GenAI) systems.
• Cloud computing experience with AWS, Azure, or GCP.

What This Role Is Not

• This is not a role focused on UI testing.
• This is not a manual Quality Assurance position.
• This role does not involve Selenium-based testing.

Hiring Note

Candidates are required to showcase practical, hands-on experience in validating ML data and models, specifically utilizing tools like Great Expectations or comparable alternatives.