MLOps Engineer

Bright Vision Technologies•Bridgewater Township, NJ

1d•Remote

About The Position

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge cloud data platform technologies to design scalable, secure, and high-performance analytics environments. As we continue to grow, we’re looking for a skilled MLOps Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Requirements

Python
TensorFlow
PyTorch
MLflow
Git
Docker containerization
Kubernetes orchestration
AWS
Azure
GCP
Terraform
DVC
Linux
Agile methodologies

Responsibilities

Architect and implement MLOps platforms to streamline Machine Learning Pipelines, from experimentation to production deployment.
Build scalable Model Deployment & Monitoring solutions using Python, TensorFlow, and PyTorch, ensuring real-time inference and drift detection.
Leverage MLflow for experiment tracking, model registry, and lifecycle management across diverse ML workflows.
Design and manage Feature Stores for efficient feature engineering, serving, and versioning to accelerate model development.
Automate CI/CD pipelines with Git, Docker containerization, and Kubernetes orchestration for seamless model updates and rollbacks.
Provision cloud-native infrastructure on AWS, Azure, or GCP using Infrastructure as Code (Terraform) for reproducible ML environments.
Implement Data Versioning practices (e.g., DVC) alongside model artifacts to maintain reproducibility and auditability.
Optimize Linux-based systems for high-performance ML operations, including GPU/TPU resource management.
Collaborate in Agile methodologies, driving iterative delivery of MLOps features through sprints and stakeholder alignment.
Monitor and scale production ML systems, proactively addressing performance issues, cost optimization, and compliance needs.