Machine Learning Engineer III

Gtatelecom; West Chester (Chester County); On-site; USD 110,000 - 170,000; Full time

Job summary

Gtatelecom is seeking a Machine Learning Engineer III to work across algorithm development, data pipeline design, model training, validation, deployment, and monitoring. You’ll help move machine learning projects from early concepts into production‑ready solutions and deliver impact across real products and applications.

This role suits an experienced engineer who enjoys depth and collaboration: design proofs‑of‑concepts, evaluate new approaches, and partner with teams beyond the immediate group

Qualifications

• Bachelor’s degree in CS, CE, Math, or related field, or equivalent industry experience.
• 5–8 years of related experience after completing a bachelor’s degree, or a master’s degree with 2–3 years of related experience
• Experience implementing, refining, and validating machine learning or deep learning algorithms
• Strong programming and software development skills
• Familiarity with Python, Java, Scala, or similar programming languages
• Experience designing data pipelines for ingestion, validation, cleaning, and monitoring

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 deploy validated models into production
• Design proofs of concept solutions and contribute to future product development studies
• Test and evaluate solutions through case studies, technical reviews, and performance reporting
• Collaborate with cross‑functional teams to address technical issues and support project delivery

Skills

ML/DL algorithms

Data pipelines

Python/Java/Scala

Model deployment

Cross-functional collaboration

Education

Bachelor's degree in CS/CE/Math

Equivalent industry experience

Tools

Kafka

Spark

Docker

Job description

Take on a hands‑on role developing machine learning solutions that support real products and applications. As a Machine Learning Engineer III, you’ll work across algorithm development, data pipeline design, model training, validation, deployment, and monitoring. You’ll help move machine learning projects from early concepts into reliable, production‑ready solutions.

This opportunity is a strong fit for an experienced engineer who enjoys both the technical depth of machine learning and the collaboration required to bring projects across the finish line. You’ll design proof‑of‑concept solutions, evaluate new approaches, work with teams beyond your immediate group, and help solve technical challenges that shape future products.

Required Skills & Experience

• 5–8 years of related experience after completing a bachelor’s degree, or a master’s degree with 2–3 years of related experience
• Experience implementing, refining, and validating machine learning or deep learning algorithms
• Strong programming and software development skills
• Familiarity with Python, Java, Scala, or similar programming languages
• Experience designing data pipelines for ingestion, validation, cleaning, and monitoring
• Bachelor’s degree in Computer Science, Computer Engineering, Mathematics, or a related technical discipline—or equivalent industry experience

Desired Skills & Experience

• Experience with data mining or statistical analysis tools
• Experience deploying and monitoring machine learning models in production
• Knowledge of Kafka, Spark, Docker, or similar data and cloud technologies
• Experience designing proof‑of‑concept solutions or contributing to technical studies
• Experience with model testing, accuracy evaluation, case studies, or performance reporting
• Experience collaborating with teams outside of an immediate work group

What You Will Be Doing

• 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 deploy validated models into production
• Design proof‑of‑concept solutions and contribute to future product development studies
• Test and evaluate solutions through case studies, technical reviews, and performance reporting
• Collaborate with cross‑functional teams to address technical issues and support project delivery

Tech Breakdown

• 30% Machine Learning Algorithm and Model Development
• 25% Data Pipeline Design and Cloud Technologies
• 20% Model Training, Validation, Deployment, and Monitoring
• 15% Proofs of Concept, Testing, and Technical Evaluation
• 10% Documentation, Collaboration, and Technical Support