New Vacancy

Let’s grow together, become our AI/ML Engineer

Eligibility Requirements

Bachelor’s or master’s in data science, AI, Computer Science or related field. 3–4 years ML engineering experience. Telecom analytics experience preferred.

Core Responsibilities

• Design, develop, train, evaluate, and refine machine learning models for use cases including customer churn prediction, revenue forecasting, anomaly detection, demand planning, customer segmentation, and network optimization.
• Collaborate with DWH and data engineering teams to design data structures that support feature stores and ML training pipelines.
• Implement security controls for AI/ML services, including access control, data protection, and secure inference.
• Conduct data exploration, statistical analysis, and hypothesis testing to identify patterns and validate model assumptions.
• Perform ML tests and statistical analysis to fine-tune model systems and improve accuracy, robustness, and interpretability.
• Extend existing ML libraries, frameworks, and internal tools to meet evolving business needs.

Competencies

• Python (Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch).
• Strong SQL skills for feature extraction, data querying, and transformation from data warehouse sources.
• Hands-on experience with Scikit-learn for classical ML model development, validation, and evaluation.

• Design, develop, train, evaluate, and refine machine learning models for use cases including customer churn prediction, revenue forecasting, anomaly detection, demand planning, customer segmentation, and network optimization.
• Collaborate with DWH and data engineering teams to design data structures that support feature stores and ML training pipelines.
• Implement security controls for AI/ML services, including access control, data protection, and secure inference.
• Conduct data exploration, statistical analysis, and hypothesis testing to identify patterns and validate model assumptions.
• Perform ML tests and statistical analysis to fine-tune model systems and improve accuracy, robustness, and interpretability.
• Extend existing ML libraries, frameworks, and internal tools to meet evolving business needs.

• Python (Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch).
• Strong SQL skills for feature extraction, data querying, and transformation from data warehouse sources.
• Hands-on experience with Scikit-learn for classical ML model development, validation, and evaluation.

• Bachelor’s or master’s in data science, AI, Computer Science or related field.