BT Global - AI Backend Developer - Python/Java

BT E SERV INDIA PRIVATE LIMITED · Bengaluru

  • Experience4–7 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Levelmid
  • Posted21 Sept 2026
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About BT E SERV INDIA PRIVATE LIMITED

BT E SERV INDIA PRIVATE LIMITED is hiring in Bengaluru in telecom. This role looks for around 4+ years of experience.

Skills

  • Generative AI
  • Large Language Models
  • Prompt Engineering
  • Agentic AI
  • Retrieval Augmented Generation
  • ReAct
  • Prompt Chaining
  • Planner-Executor
  • Machine Learning
  • Predictive Analytics
  • Reinforcement Learning
  • Operations Research
  • Workforce Optimization
  • Constraint-Based Scheduling
  • Route Optimization
  • Mathematical Modelling
  • Simulation
  • Python
  • Java
  • Node.js
  • APIs
  • Microservices Architecture
  • Cloud-native platforms
  • Enterprise System Integrations
  • Vector Databases
  • Azure AI Services
  • Azure OpenAI
  • AWS Bedrock
  • Model Context Protocol
  • DevSecOps
  • CI/CD
  • Observability
  • Site Reliability Engineering

The role

A generative AI engineer at a telecommunications company designs intelligent workflow systems using Generative AI, Retrieval Augmented Generation, and Operations Research, building scalable backend services and optimizing workforce decisions with Machine Learning and Python. The work also applies Reinforcement Learning and DevSecOps to reliable AI-enabled operational platforms.

Full job description

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.