Machine Learning 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. 

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 a Machine Learning Engineer to develop, train, evaluate, and operationalize predictive and classification models for production applications. The engineer will work across data preparation, feature engineering, model experimentation, validation, and deployment, partnering with data scientists and software teams to deliver measurable model performance and maintainable services. 

Key Responsibilities 

• Prepare datasets, engineer features, and build reproducible training and validation pipelines. 

• Develop, tune, and evaluate machine learning models for classification, regression, ranking, or forecasting use cases. 

• Implement model-serving components and integrate inference workflows with application and data platforms. 

• Track model performance, investigate drift or quality degradation, and recommend retraining or improvement actions. 

• Document experiments, model assumptions, evaluation results, and implementation decisions for engineering stakeholders. 

Required Qualifications 

• Experience Required: typically 5+ years in machine learning engineering, applied data science, or related production model development; align with the approved requisition. 

• Strong Python skills and practical experience with libraries such as scikit-learn, pandas, NumPy, or equivalent tools. 

• Understanding of supervised and unsupervised learning, model evaluation, feature engineering, and statistical fundamentals. 

• Experience with at least one deep-learning framework such as PyTorch or TensorFlow, where relevant to the project. 

• Experience building reproducible data and model pipelines and managing code through Git. 

• Ability to deploy or integrate model inference into APIs, batch workflows, or application services. 

• Strong analytical reasoning, experimentation discipline, and communication of model trade-offs. 

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

Preferred Qualifications 

• Experience with distributed training, feature stores, experiment tracking, or model registries. 

• Exposure to cloud machine learning services and containerized deployment. 

• Familiarity with model explainability, bias evaluation, and production monitoring practices. 

• Relevant machine learning or cloud platform 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.