ML Engineer

ML Engineer & Lead ML Engineer

Design, develop, and deploy scalable machine learning models and systems specifically addressing telecom industry challenges. Collaborate with data scientists and engineers to build robust ML solutions leveraging telecom domain knowledge (like Wireline, Wireless, NQES, Sites, Interfaces, churn prediction, real-time data processing, SINR, Video on Demand, Fixed Wireless Access (FWA), and emerging telecom trends. Ensure production-grade code quality and system reliability for large-scale data environments.

Develop, test, and deploy ML models tailored for telecom use cases such as churn prediction, network optimization, SINR analysis, and QoS improvement.

• Collaborate with data scientists to translate prototypes into scalable, production-ready systems handling large-scale telecom data.
• Build and optimize data pipelines for real-time and batch processing of telecom datasets including wireline and wireless network data.
• Write clean, efficient, and maintainable code adhering to best practices and telecom-specific data compliance standards.
• Conduct code reviews and help troubleshoot model and system integration issues in telecom environments.
• Stay current with telecom trends and incorporate domain-specific knowledge into ML solutions.

Strong proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn).

• Experience with large-scale data processing frameworks such as Spark and Hadoop, particularly for telecom datasets.
• Composer, Data Proc over GCP, Vertex.AI, BigQuery, Teradata
• Understanding H2O is a plus
• Solid understanding of telecom domain concepts: Wireline, Wireless, QES, Sites, Interfaces, churn, real-time data, FWA.
• Knowledge of containerization (Docker) and orchestration (Kubernetes) in cloud environments (AWS, GCP, Azure).
• Ability to work with streaming data platforms and real-time analytics.
• Strong problem-solving skills and collaborative mindset.

Lead the end-to-end design, development, and deployment of machine learning solutions addressing telecom industry challenges. Mentor engineering teams while driving integration of telecom domain expertise into ML projects focused on wireline, wireless, real-time network analysis, churn, and emerging telecom technologies.

• Lead ML project delivery including data collection, feature engineering, model development, and production deployment for telecom applications.
• Architect scalable ML systems optimized for processing large volumes of telecom data (sites, interfaces, NQES, SINR).
• Mentor junior engineers on ML best practices, telecom domain specifics, and large-scale system design.
• Collaborate with data scientists, product managers, and business teams to align ML projects with telecom business goals.
• Enforce coding standards, testing, and documentation in telecom ML workflows.
• Research and incorporate latest telecom trends and technologies into ML systems.

Extensive experience with ML frameworks (TensorFlow, PyTorch) and distributed data platforms (Spark, Hadoop) in telecom contexts.

• Strong leadership and project management experience managing telecom-focused ML teams.
• Expertise in cloud ML services (AWS SageMaker, GCP AI Platform) and container orchestration (Kubernetes).
• Deep understanding of telecom domain concepts and ability to translate them into scalable ML architectures.
• Composer, Data Proc over GCP, Vertex.AI, BigQuery, Teradata
• Understanding H2O is a plus