Accountable for design, develop, and operationalize Artificial Intelligence (AI) and Machine Learning (ML) solutions that enhance network efficiency, customer experience, and operational automation.

Focus on leveraging AI to optimize network performance, predictive maintenance, fraud detection, customer analytics, and intelligent automation, aligning with the company’s digital transformation and data strategy.

1. Develop and execute AI strategies aligned with company strategy objectives.

2. Develop and deploy AI/ML models for use cases such as network optimization, churn prediction, anomaly detection, and traffic/revenue/sales forecasting.

3. Integrate AI solutions with existing OSS/BSS systems, data lakes, and analytics platforms.

4. Collaborate with data engineering teams to ensure data quality, accessibility, and governance for AI workloads.

5. Research and evaluate emerging AI technologies (e.g., LLMs, edge AI, computer vision for site inspections).

6. Lead AI innovation initiatives and pilot emerging technologies.

7. Automate operational workflows through AI-driven insights and AI-Agentic models.

8. Translate business problems into data science and AI initiatives with measurable impact.

9. Document AI models, pipelines, and processes following MLOps and data governance frameworks.

   10.  Provide technical guidance and mentoring to cross-functional teams on AI tools, methods, and best practices.

   11.  Collaborate with vendors, partners, and academic institutions on AI innovation projects.

   12.  Ensure AI systems comply with data privacy, security, and ethical standards.

   13.  Manage the lifecycle of AI models including training, validation, deployment, and monitoring.

   14.  Mentor and manage AI team members, fostering a culture of continuous learning and experimentation.

• Accuracy and reliability of deployed AI models.

• Reduction in churn rate, fraud incidents, or network downtime due to AI interventions.

• Time-to-deployment for AI solutions.

• Compliance with data governance and privacy regulations.

• Number of AI-driven use cases implemented and adopted.

• Reports to Data and AI Director.

• Responsible for company job related assets.

• Adhering to the standard Processes.

• Bachelor’s or Master’s degree in Artificial Intelligence, Computer Science, Data Science, or related field.

• 5+ years of experience in AI/ML, with at least 2 years in telecom or related industries.

• Proven track record of leading AI projects from concept to deployment.

• Experience working with telecom data sources (e.g., CDRs, NMS logs, OSS/BSS, IoT sensor data).

• Hands-on experience with Python, TensorFlow, PyTorch, or similar frameworks

• Familiarity with MLOps pipelines, CI/CD for ML, and containerized environments (Docker, Kubernetes).

• Exposure to big data ecosystems (Hadoop, Spark, Kafka, Hive) and data governance frameworks.

• Advantage: Experience with LLMs, NLP, or computer vision for telecom customer and network data.

• Deep understanding of AI/ML algorithms, model evaluation, and deployment pipelines.

• Model deployment (APIs, microservices, MLOps).

• Knowledge of data governance, privacy laws (e.g., GDPR), and ethical AI practices.

• Understanding of network automation and AI-driven service assurance.

• Excellent experience in SQL, PL/SQL.

• Good knowledge in Big data applications such as Hadoop, Hive, Spark.

• Familiarity with data architecture.

• Having good knowledge in one of standard BI tool.

• Ability to interpret and apply best practice methods/processes.

• Strong analytical and problem-solving skills.

• Project management and agile methodology expertise.

• Leadership and team development capabilities.

• Excellent communication and stakeholder engagement abilities.

• Change management.

• Statistical analysis and optimization.

• Presentation and storytelling with data.

• Business translation of AI outcomes.

• Innovation and adaptability to emerging technologies.

• Constantly seeks new ways to apply AI to telecom challenges.

• Collaboration and cross-functional influence.

• Results Drive.

• Ethical mindset and commitment to responsible AI.

• Being Collaborative.

• Tenacity, Resilience and Self-Management.