ML Engineer II: From Concept to Production

Gtatelecom; West Chester (Chester County); On-site; USD 110,000 - 160,000; Full time

Job summary

Gtatelecom is seeking an experienced Machine Learning Engineer II to develop solutions across algorithm design, data pipelines, model validation, and production deployment. You will turn ideas into dependable technical solutions through hands-on coding, testing, and thorough documentation.

You will work independently on initiatives while collaborating with teams, from proof-of-concept to monitoring deployed models, gaining broad exposure to the full ML lifecycle and production readiness.

Qualifications

• 3–5 years of related experience after completing a bachelor’s degree or a recent master’s degree.
• Experience implementing, refining, and validating ML or deep learning algorithms.
• Strong programming and software development skills.
• Familiarity with Python, Java, Scala, or similar languages.
• Experience designing or supporting data pipelines for ingestion, validation, cleaning, and monitoring.
• Bachelor’s degree in CS/CE/Math or related discipline—or equivalent industry experience.

Responsibilities

• Implement, refine, and validate machine learning algorithms for products and applications.
• Design and develop data pipelines for data ingestion, validation, cleaning, and monitoring.
• Train models, evaluate accuracy, and support deployment of validated models into production.
• Design proofs of concept and contribute to future product development efforts.
• Test and evaluate solutions through case studies, technical reviews, and reporting.
• Collaborate with cross‑functional teams to address technical issues and support project delivery.

Skills

ML algorithms

Data pipelines

Python

Java/Scala

Model deployment

Education

Bachelor’s degree in CS/CE/Math

Tools

Kafka

Spark

Docker

Job description

Gtatelecom is seeking an experienced Machine Learning Engineer II to develop solutions across algorithm design, data pipelines, model validation, and production deployment. You will turn ideas into dependable technical solutions through hands-on coding, testing, and thorough documentation.

You will work independently on initiatives while collaborating with teams, from proof-of-concept to monitoring deployed models, gaining broad exposure to the full ML lifecycle and production readiness.