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.
- Robust 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.

📌 AI Backend Developer (Bengaluru)
🏢 BT Group
📍 Bengaluru