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 & AI Architect (India)
🏢 Monty Mobile
📍 India