Onsite Tampa ML Engineer — End-to-End ML Pipelines (3-mo)

NextPathAI; Tampa (FL); On-site; USD 76,000 - 90,000; Full time

Job summary

NextPathAI in Tampa, FL is seeking a Machine Learning Engineer for a 3-month onsite contract. You will research, build, deploy, and monitor ML solutions, partnering with data scientists and software engineers to deliver actionable insights and predictions across the ML lifecycle.

Responsibilities include developing models with TensorFlow, PyTorch, or Scikit-learn; building end-to-end pipelines; deploying models to production; and communicating results to both technical and non-technical

Qualifications

• Bachelor’s degree or higher in CS, Engineering, Math, Statistics, or related field.
• Experience applying ML to real-world use cases such as NLP, computer vision, recommender systems, or anomaly detection.
• Proficiency in Python and libraries including NumPy, Pandas, and Matplotlib.
• Familiarity with ML frameworks TensorFlow, PyTorch, or Scikit-learn, and cloud platforms AWS, Azure, or GCP.
• Knowledge of data quality, feature engineering, model validation, and hyperparameter tuning.
• Strong problem-solving, analytical skills, and ability to work independently or as part of a team.

Responsibilities

• Research, develop, and optimize ML algorithms and models.
• Build end-to-end ML pipelines including data collection, preprocessing, model training, and evaluation.
• Deploy and monitor ML models in production for scalability and performance.
• Collaborate with stakeholders to translate business goals into ML solutions.
• Communicate results to both technical and non-technical audiences.

Skills

Python

NumPy

Pandas

Matplotlib

Education

Bachelor’s degree or higher in CS/Engineering/Math/Statistics

Tools

TensorFlow

PyTorch

Scikit-learn

AWS

Azure

GCP

Job description

NextPathAI in Tampa, FL is seeking a Machine Learning Engineer for a 3-month onsite contract. You will research, build, deploy, and monitor ML solutions, partnering with data scientists and software engineers to deliver actionable insights and predictions across the ML lifecycle.

Responsibilities include developing models with TensorFlow, PyTorch, or Scikit-learn; building end-to-end pipelines; deploying models to production; and communicating results to both technical and non-technical