Prodapt is seeking a Lead ML Engineer to design, develop, and deploy machine learning solutions for the telecom industry, focusing on challenges like network analysis, churn, and emerging technologies.
Requirements
- Extensive experience with ML frameworks (TensorFlow, PyTorch) and distributed data platforms (Spark, Hadoop) in telecom contexts.
- 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
Responsibilities
- 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.
Other
- 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.
- Strong leadership and project management experience managing telecom-focused ML teams.
