Senior ML Engineer - End-to-End Pipelines & MLOps

Quantum Technologies. LLC; Atlanta (GA); In loco; USD 120.000 - 180.000; Tempo pieno

Descrizione del lavoro

Quantum Technologies. LLC is seeking an experienced Machine Learning Engineer to design and deploy scalable ML models for complex business problems in a fast-paced environment.

You will work with large-scale data, build end-to-end ML pipelines, and collaborate across data, product, and engineering teams to deliver production-ready solutions. The role emphasizes model performance optimization, MLOps practices, and governance considerations, with exposure to cloud platforms (AWS/Azure/GCP) and

Competenze

• 6–11 years of ML engineering experience.
• Strong Python proficiency.
• Hands-on with ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Experience with data processing frameworks like Spark, Pandas, NumPy.
• Strong understanding of ML algorithms, statistics, and model evaluation.
• Experience deploying models via REST APIs, Docker, Kubernetes.
• Solid knowledge of SQL and working with large databases.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Strong problem-solving and communication skills.

Mansioni

• Design, develop, and deploy scalable ML models for real-world business problems.
• Work with large-scale structured and unstructured datasets.
• Build and maintain end-to-end ML pipelines (data ingestion, feature engineering, model training, evaluation, deployment).
• Collaborate with Data Engineers, Data Scientists, and Product teams.
• Optimize model performance, accuracy, and efficiency.
• Implement MLOps best practices including monitoring, versioning, retraining.

Conoscenze

ML engineering

Python

communication

Strumenti

TensorFlow

PyTorch

Scikit-learn

Spark

Pandas

NumPy

Docker

Kubernetes

SQL

AWS/Azure/GCP

Descrizione del lavoro

Quantum Technologies. LLC is seeking an experienced Machine Learning Engineer to design and deploy scalable ML models for complex business problems in a fast-paced environment.

You will work with large-scale data, build end-to-end ML pipelines, and collaborate across data, product, and engineering teams to deliver production-ready solutions. The role emphasizes model performance optimization, MLOps practices, and governance considerations, with exposure to cloud platforms (AWS/Azure/GCP) and