Sr Manager - IoT and Private Networks

You will be responsible for architecting and deploying solutions that combine machine learning models with full stack applications using Java and Python. Your main focus will be on integrating data pipelines, model inference, and API-driven front-end interfaces to automate workflows and optimize performance across systems. You will drive implementation strategies aligned to product requirements and engineering standards. Your responsibilities will include understanding IoT-specific requirements such as data ingestion from edge devices, analytics needs, and user-facing application features. You will lead technical discussions with cross-functional teams to evaluate feasibility, define specifications, and assess performance and scalability for IoT solutions. It will be your responsibility to define and design software architecture for integrating IoT data pipelines, ML models, and full stack applications using Java and Python. You will also deliver robust features including sensor data processing, real-time analytics dashboards, and APIs for device management and control. Moreover, you will drive the deployment of end-to-end IoT platforms from data collection and ML model deployment to web/mobile access with a focus on automation and resilience. You will review and finalize infrastructure design including edge-cloud integration, containerized services, and streaming data solutions such as Kafka and MQTT. Creating and managing user stories for device-side logic, cloud-based processing, and visualizations will also be a part of your responsibilities. You will establish standards for edge computing, MLOps in IoT, and cloud-native application development, ensuring security, scalability, and maintainability. Furthermore, you will facilitate the prioritization of features related to device data processing, predictive maintenance, anomaly detection, and real-time user interfaces. Desired Skill Sets: - Strong experience architecting and delivering software applications combining real-time data, machine learning, and cloud-native full stack platforms. - Hands-on expertise with Java (Spring Boot) and Python for both backend services and ML model implementation. - Experience with IoT protocols (MQTT, CoAP), data streaming (Kafka, AWS Kinesis), and edge-cloud data integration. - Deep understanding of software/application lifecycle management for connected device platforms. - Experience working in Agile setups and DevOps pipelines with tools like Docker, Kubernetes, Jenkins, Git.,