Senior Machine Learning Engineer

FirstPRO 360; Atlanta (GA); On-site; USD 150,000 - 210,000; Full time

Job summary

FirstPRO 360 seeks a hands-on Senior Machine Learning Engineer to architect, build, and own end-to-end production ML systems for a growing enterprise SaaS platform in Atlanta. You will develop intelligent systems that classify, normalize, and enrich large volumes of complex business data using ML, NLP, and modern AI tech.

The role requires ownership of end-to-end ML pipelines, strong Python and frameworks (PyTorch/TensorFlow), and deep experience deploying models with MLOps, monitoring, and

Qualifications

• 6+ years of ML Engineering, Applied AI, or related experience.
• Strong Python and experience with PyTorch, TensorFlow, or scikit-learn.
• Proven end-to-end ownership of production ML systems.
• Experience with classification, NLP, entity matching, or similar modeling challenges.

Responsibilities

• Architect and develop end-to-end production ML systems.
• Build classification, categorization, entity-matching, and data-normalization models.
• Develop scalable ML pipelines for training, inference, and deployment.
• Implement confidence scoring, model monitoring, drift detection, and retraining.
• Leverage Python, PyTorch, NLP, embeddings, and LLMs where appropriate.
• Build reliable MLOps infrastructure supporting multiple enterprise customers.
• Own technical decisions and collaborate with engineering, data science, and product teams.

Skills

Python

PyTorch

TensorFlow

scikit-learn

NLP

MLOps

Communication

Architecture

Tools

Docker

Kubernetes

AWS SageMaker

Job description

We are seeking a hands-on Senior Machine Learning Engineer to architect, build, and own end-to-end production ML systems for a growing enterprise SaaS platform.

This role focuses on developing intelligent systems that classify, normalize, and enrich large volumes of complex business and transactional data using machine learning, NLP, and modern AI technologies.

Responsibilities

• Architect and develop end-to-end production ML systems.
• Build classification, categorization, entity-matching, and data-normalization models.
• Develop scalable ML pipelines for training, inference, and deployment.
• Implement confidence scoring, model monitoring, drift detection, and retraining.
• Leverage Python, PyTorch, NLP, embeddings, and LLMs where appropriate.
• Build reliable MLOps infrastructure supporting multiple enterprise customers.
• Own technical decisions and collaborate with engineering, data science, and product teams.

Requirements

• 6+ years of ML Engineering, Applied AI, or related experience.
• Strong Python and experience with PyTorch, TensorFlow, or scikit-learn.
• Proven end-to-end ownership of production ML systems.
• Experience with classification, NLP, entity matching, or similar modeling challenges.
• Strong MLOps experience, including deployment, monitoring, and retraining.
• Experience with AWS, SageMaker, Docker, Kubernetes, or comparable cloud infrastructure.
• Excellent problem-solving, architecture, and communication skills.

Ideal Candidate: An engineer who can independently design, build, deploy, monitor, and improve scalable machine learning systems - not simply train models or maintain existing infrastructure.