Future Connections

• Own the technical design of Configuration Management capabilities within a complex, multi-vendor Core Network environment, ensuring scalability and resilience. • Lead a delivery squad: you set direction and review design, while your team handles day-to-day platform coding. • Make and defend technical decisions in front of the customer's Core Engineering team. • Drive the translation of operator change practices — change windows, CMDB updates, rollback expectations — into automated, auditable gates. • Own vendor and data-model engagement across Ericsson, Huawei and other in-scope vendors, including YANG modules and NETCONF exports. • Be the technical escalation point for the account, from vendor engagement to lab access and integration with the customer's change-management systems.

• Design and implement LLM-based agents for OSS tasks • Automate and augment OSS workflows • Integrate agents with OSS/NMS systems • Reason over network telemetry • Build evaluation and monitoring frameworks for agent accuracy

• Design, develop and deploy advanced AI/ML models for telecom use cases such as: Network optimization, Traffic prediction and anomaly detection, QoS/QoE modeling and root cause analysis • Apply and adapt state-of-the-art AI techniques, including: Deep Learning (CNNs, RNNs, Transformers), Reinforcement Learning for network optimization, Time-series modeling at scale • Work deeply with real telecom network data (3G/4G/5G), understanding KPIs, counters, and traffic dynamics • Build production-ready solutions, ensuring scalability, performance, and reliability • Collaborate closely with data engineers and telecom experts to integrate AI models into real systems • Act as a technical reference within the team, contributing to best practices and code quality • Explore and apply Generative AI and LLM-based approaches to telecom use cases

• Design, develop and maintain backend services in Python • Build and optimize ETL pipelines for large-scale data processing • Work with structured and unstructured data, ensuring data quality and consistency • Develop data ingestion, transformation, and integration processes • Deploy and manage applications in cloud environments (AWS, Azure or GCP) • Containerize applications using Docker and contribute to scalable architectures • Analyze datasets to extract insights and support technical decision-making • Ensure code quality, performance, and maintainability