Senior ML Engineer: Build Scalable AI & Production Pipelines

CodeX Tech-IT LLC; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

CodeX Tech-IT LLC is seeking an experienced Machine Learning Engineer to join our technology team. You will develop, deploy, and maintain ML models and help build scalable AI-driven solutions.

Responsibilities include designing, training, deploying models, preparing datasets, feature engineering, evaluating and optimizing performance, building scalable ML pipelines, collaborating with data scientists, software engineers, and product teams, and monitoring in production while maintaining

Qualifications

• 3+ years of professional experience in Machine Learning or a related field.
• Strong proficiency in Python.
• Experience with TensorFlow, PyTorch, or Scikit-learn.
• Strong knowledge of machine learning algorithms and statistics.
• Experience with SQL and data-processing techniques.
• Knowledge of AWS, Azure, or Google Cloud.
• Understanding of MLOps and model deployment practices.

Responsibilities

• Design, develop, train, and deploy machine learning models.
• Prepare datasets and perform feature engineering.
• Evaluate, optimize, and improve model performance.
• Develop scalable machine learning pipelines.
• Collaborate with data scientists, software engineers, and product teams.
• Deploy and monitor ML models in production environments.
• Maintain technical documentation and follow engineering best practices.

Skills

Python

TF/PyTorch/Sklearn

SQL

Cloud platforms

MLOps

NLP/Computer Vision (pref)

Docker

Kubernetes

Education

Bachelor's or Master's in CS/DS/AI

Tools

Docker

Kubernetes

Job description

CodeX Tech-IT LLC is seeking an experienced Machine Learning Engineer to join our technology team. You will develop, deploy, and maintain ML models and help build scalable AI-driven solutions.

Responsibilities include designing, training, deploying models, preparing datasets, feature engineering, evaluating and optimizing performance, building scalable ML pipelines, collaborating with data scientists, software engineers, and product teams, and monitoring in production while maintaining