Role & responsibilities :

- Lead the technical transformation of Openreach scheduling and inventory platforms into AI-powered, cloud-native, scalable ecosystems that improve operational efficiency and engineering productivity.

- Design, develop, deploy, and support AI-powered agents, copilots, automation solutions, and intelligent workflow systems.

- Build next-generation AI-driven scheduling, workforce optimization, and decision-support capabilities for large-scale operational environments.

- Design and implement Retrieval Augmented Generation (RAG), ReAct, Prompt Chaining, Planner-Executor, and other agentic AI workflows.

- Develop AI-enabled services using Large Language Models (LLMs) and supporting technologies.

- Build and maintain APIs, microservices, and integrations between AI solutions and enterprise systems.

- Implement AI capabilities across the Software Development Lifecycle (SDLC) using AI-assisted development tools such as GitHub Copilot, Cursor, Amazon Q, and Kiro.

- Develop and maintain prompt libraries, reusable AI components, context management, and memory frameworks.

- Design workforce optimization, constraint-based scheduling, route optimization, recommendation engines, forecasting, and simulation models.

- Apply Machine Learning, Predictive Analytics, Reinforcement Learning, and Operations Research techniques to complex business challenges.

- Monitor AI solution performance and continuously improve accuracy, hallucination rates, task success rates, latency, operational costs, and user adoption.

- Ensure engineering excellence through security, DevSecOps, CI/CD, observability, reliability, scalability, and SRE best practices.

- Collaborate with architects, engineers, product teams, and business stakeholders to deliver AI initiatives and drive adoption of AI-enabled ways of working.

Skills & Experience:

- 4 - 7 years of Software Engineering experience with strong expertise in AI-enabled solutions.

- Strong hands-on experience in Generative AI, Large Language Models (GPT, Claude, Gemini, etc.), Prompt Engineering, and Agentic AI solutions.

- Experience designing and implementing RAG architectures and AI design patterns including ReAct, Prompt Chaining, Planner-Executor, and Chain-of-Thought prompting.

- Expertise in Machine Learning, Predictive Analytics, and Reinforcement Learning.

- Strong knowledge of Operations Research, Workforce Optimization, Constraint-Based Scheduling, Route Optimization, Mathematical Modelling, and Simulation.

- Proficiency in Python and Java/Node.js with experience building scalable backend systems and AI services.

- Experience with APIs, Microservices Architecture, Cloud-native platforms, and Enterprise System Integrations.

- Knowledge of Vector Databases such as Pinecone, FAISS, Azure AI Search, or similar platforms.

- Experience with Azure AI Services, Azure OpenAI, AWS Bedrock, or equivalent AI cloud platforms.

- Understanding of MCP (Model Context Protocol) concepts and AI application architecture.

- Experience implementing AI capabilities within SDLC using AI-native development tools.

- Expertise in performance optimisation, high-performance system design, DevSecOps, CI/CD, observability, and Site Reliability Engineering (SRE).

- Experience building large-scale operational decision-support systems supporting workforce planning and optimization.