Senior Software Engineer Machine Learning (Telecom Domain)

Overview

On Site

Depends on Experience

Contract - W2

Contract - Independent

No Travel Required

Skills

Algorithms

Continuous Integration

Kubernetes

Databricks

Machine Learning (ML)

Network Optimization

Lifecycle Management

Machine Learning Operations (ML Ops)

Software Engineering

Predictive Modelling

Good Clinical Practice

Docker

Job Details

Job Title: Senior Software Engineer Machine Learning (Telecom Domain)

Location: Irving, TX

Duration: Long-Term Contract

Job Summary:

We are looking for a skilled Senior Software Engineer Machine Learning (ML) with solid experience in the Telecom domain. The ideal candidate will develop and deploy ML models that drive intelligent automation, network optimization, and predictive analytics for telecom operations.

Key Responsibilities:

Design, build, and deploy scalable machine learning models and data pipelines.

Collaborate with data engineers and scientists to implement ML solutions for telecom data.

Optimize model performance and ensure production-grade reliability.

Work with large telecom datasets for predictive modeling and anomaly detection.

Implement ML workflows using Python, TensorFlow, PyTorch, and Scikit-learn.

Leverage cloud ML platforms (Google Cloud Platform Vertex AI, AWS SageMaker, or Azure ML).

Required Skills & Experience:

10+ years of experience in software engineering or ML development.

Telecom domain experience is mandatory.

Proficient in Python, SQL, Spark, and ML frameworks (TensorFlow/PyTorch).

Strong understanding of data structures, algorithms, and ML lifecycle management.

Hands-on with CI/CD for ML, containerization (Docker/Kubernetes).

Preferred:

Familiarity with MLOps practices and cloud-native deployments.

Experience with BigQuery, Databricks, or Airflow.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.