Azure ML Engineer (NLP, MLOps, Cloud AI)

Solomon Page; United States; On-site; USD 48,216 - 79,900; Full time

Benefits offered by this job

Medical insurance

Dental insurance

401(k)

Direct deposit

Commuter benefits

Job summary

Solomon Page is seeking a Machine Learning Engineer to design, develop, deploy, and optimize ML solutions supporting enterprise AI initiatives. You will work with data scientists and engineers to build scalable cloud-based AI systems using Python, TensorFlow, PyTorch, Keras, and Azure.

The role emphasizes ML model development, NLP tasks, MLOps pipelines, and collaboration with business stakeholders to translate requirements into actionable AI solutions.

Qualifications

• Bachelor’s or Master’s degree in a relevant field.
• Hands-on ML development with supervised/unsupervised learning.
• Proficiency in NLP and neural networks, with Python experience.
• Experience with ML frameworks and cloud AI services.

Responsibilities

• Design, develop, train, and deploy ML models for enterprise apps.
• Build scalable NLP solutions for text processing and classification.
• Implement and optimize deep learning models using TensorFlow, Keras, PyTorch.
• Develop ML pipelines and deployment workflows with MLOps best practices.
• Collaborate across data science, software, and business teams to deliver AI solutions.
• Deploy and monitor models in Microsoft Azure cloud environments.

Skills

Machine Learning

Supervised & Unsupervised Learning

Neural Networks

Natural Language Processing (NLP)

Python

R

SQL

Azure

MLOps

DevOps

Education

Bachelor's or Master's degree in Computer Science, Data Science, ML/AI

Tools

TensorFlow

Keras

PyTorch

Microsoft Azure

Job description

Solomon Page is seeking a Machine Learning Engineer to design, develop, deploy, and optimize ML solutions supporting enterprise AI initiatives. You will work with data scientists and engineers to build scalable cloud-based AI systems using Python, TensorFlow, PyTorch, Keras, and Azure.

The role emphasizes ML model development, NLP tasks, MLOps pipelines, and collaboration with business stakeholders to translate requirements into actionable AI solutions.