Software Engineering Professional

We are looking for a highly skilled professional to modernise a mission-critical scheduling platform, redesign optimisation algorithms, and embed AI-driven decision intelligence into a system that manages over 20,000 field engineers and partner resources. The role focuses on improving operational efficiency, scalability, and decision-making through advanced technology and intelligent automation.

This opportunity goes beyond traditional software engineering and requires a strong blend of engineering, data science, AI, and optimisation expertise. Candidates with experience in scheduling optimisation, operations research, machine learning, and large-scale platform modernisation will be particularly well suited, as these capabilities are critical to solving complex workforce planning and operational challenges.

• Lead the transformation of Openreach's Taskforce Scheduling Platform from a legacy scheduling solution to a modern, AI-driven, cloud-native platform.
• Design and deliver intelligent scheduling capabilities that optimise the deployment of 20,000+ field engineers across multiple work types and operational constraints.
• Drive technology decisions that improve operational efficiency, customer experience, and business outcomes.

• Strong experience in Machine Learning, Predictive Analytics, and Generative AI/LLM-based decision support solutions.
• Expertise in Reinforcement Learning, Operations Research, Workforce Optimisation, Constraint-Based Scheduling, Route Optimisation, and Mathematical Modelling.
• Proficiency in Java, Python, high-performance system design, performance optimisation, DevSecOps, CI/CD, and observability/SRE practices.

• Experience with Feature Stores, Vector Databases, and Data Lakes for scalable AI and analytics solutions.
• Knowledge of MLOps and AI Model Lifecycle Management frameworks and best practices.
• Familiarity with Agentic AI Frameworks and Knowledge Graphs to support advanced AI-driven applications.
• Thinks holistically about systems and challenges existing approaches to drive better outcomes.
• Comfortable working in ambiguity and solving complex optimisation problems while balancing innovation with practical delivery.
• Demonstrates curiosity, continuous learning, a product-focused mindset, and a passion for applying AI to solve real-world challenges at scale.