Job Objective Lead the design and architecture of the SRP Data & AI platform, responsible for processing large volumes of telecommunications network data and transforming them into actionable intelligence for cellular-based subscriber geolocation. The architect will define the end-to-end data and AI architecture, covering data ingestion, distributed processing, data engineering, machine learning pipelines, feature engineering, model integration, and analytics. The role requires a strong combination of enterprise data architecture, big data technologies, and applied AI/ML expertise. Main Tasks
• Design the end-to-end Data & AI architecture supporting the SRP platform.
• Define scalable data ingestion and processing architectures for large-scale 4G/LTE and 5G network datasets.
• Architect distributed data processing pipelines for cellular network information, including Cell ID, Timing Advance, RSRP, RSRQ, SINR, and other radio measurements.
• Design data lakes, lakehouses, data warehouses, and data management frameworks.
• Define architectures for batch and real-time data processing.
• Design feature engineering and data preparation pipelines for geolocation and machine learning algorithms.
• Establish data quality, governance, metadata management, lineage, and data security frameworks.




• Architect the integration of ML models, AI services, and geolocation engines into the platform.
• Define scalable AI/ML infrastructure and model-serving architectures.
• Evaluate technologies in data platforms.
• Work with ML engineers, data scientists, and telecom specialists to translate SRP requirements into technical architecture.
• Establish performance, scalability, reliability, and cost-optimization standards. Qualifications
• Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, or a related discipline.
• 8+ years of experience in data architecture, big data engineering, or enterprise AI platforms.
• Demonstrated experience designing large-scale distributed data processing systems.
• Experience with Kafka, Flink, distributed computing, and real-time data processing.
• Robust knowledge of cloud platforms, preferably AWS, Azure, or GCP.
• Experience designing data pipelines for machine learning and AI workloads.
• Understanding of MLOps, feature stores, model deployment, and AI platform architecture.
• Experience with Python, SQL, and modern data engineering frameworks.
• Knowledge of telecommunications data or time-series and geospatial datasets is an advantage.

📌 Senior Data (India)
🏢 Monty Mobile
📍 India