MLOps Engineer

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is an opportunity to contribute to technology initiatives in a collaborative environment with opportunities for professional growth.

Job Title: MLOps Engineer

Location: Remote

Position Type: Full-time, Direct W2 - [Not open to C2C or 1099]

Salary Range: $115,000–$165,000 Annually

Experience Required: 5+ years

Sponsorship: U.S. Citizens, Green Card holders, EAD holders, and H-1B transfer candidates are encouraged to apply. Bright Vision Technologies is unable to sponsor new H-1B visa petitions for this position.

Job Summary

Bright Vision Technologies is seeking an MLOps Engineer to build and maintain the automation and infrastructure required to develop, deploy, monitor, and manage machine learning models in production. The role will establish reproducible pipelines, model versioning and registry practices, automated deployment, and operational monitoring in partnership with data science and platform engineering teams.

Key Responsibilities

• Build automated pipelines for data validation, model training, evaluation, packaging, and deployment.

• Implement model versioning, experiment tracking, artifact management, and model registry workflows.

• Automate deployment and serving of models using containers, orchestration platforms, and approved cloud services.

• Establish monitoring for model availability, latency, resource utilization, data quality, and model drift.

• Collaborate with data scientists, application engineers, and platform teams to improve reproducibility, reliability, and release governance.

Required Qualifications

• Experience Required: typically 5+ years in DevOps, machine learning engineering, or ML platform operations; align with the approved requisition.

• Strong Python or comparable scripting skills and practical experience automating engineering workflows.

• Experience with Docker and Kubernetes or comparable container deployment technologies.

• Working knowledge of CI/CD, Git, infrastructure automation, and cloud-based deployment practices.

• Understanding of machine learning lifecycle concepts, model packaging, experiment tracking, and model serving.

• Experience with monitoring, logging, troubleshooting, and production support for data or application services.

• Strong collaboration skills working with data science, software development, and infrastructure teams.

• Experience working in iterative software delivery teams, participating in planning, defect triage, and release validation as appropriate to the assignment.

Preferred Qualifications

• Experience with MLflow, Kubeflow, SageMaker, Azure Machine Learning, or Vertex AI.

• Familiarity with feature stores, model registries, data/model drift monitoring, and pipeline orchestration.

• Exposure to Terraform, Helm, workflow schedulers, and scalable inference systems.

• Relevant cloud, Kubernetes, DevOps, or machine learning operations certification.

How to Apply

Would you like to know more about this opportunity? For immediate consideration, please send your resume to Jenny@bvteck.com or contact Jennifer at (908) 505-3544. Learn more about Bright Vision Technologies at www.bvteck.com.

Bright Vision Technologies is an Equal Opportunity Employer.

Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.

BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.