Senior Design Engineer – AI / LLM / RAG Platform

Mavenir; Gurugram District; On-site; INR 3,800,000 - 6,000,000; Full time

Job summary

Mavenir in Gurugram, India, seeks a strong Python engineer with GenAI/LlM/RAG experience to design AI-powered service assurance tools for telecom operations. You will build scalable backend services, APIs, and AI copilots that automate troubleshooting, RCA, and workflow orchestration.

You will implement prompt engineering, embeddings, and semantic search, while grounding AI outputs and ensuring robust observability and CI/CD.

Qualifications

• 4-8 years of software/platform engineering experience.
• 1-3 years of hands-on experience in GenAI, LLM, RAG, AI Assistants, or Agentic AI solutions.

Responsibilities

• Design and develop LLM-powered operational assistants and copilots.
• Build and optimise RAG pipelines using enterprise knowledge, alarms, KPIs, logs, tickets, and documentation.
• Develop Agentic AI workflows for RCA, alarm analysis, incident triage, and recommendation generation.
• Implement prompt engineering, embeddings, semantic search, and response evaluation.
• Improve AI accuracy, grounding, and explainability.
• Develop scalable backend services using Python and build APIs/microservices with FastAPI/Flask.
• Create automation pipelines for data ingestion, analysis, and AI orchestration.
• Deliver production-quality, maintainable, and testable code.
• Develop and deploy applications on Kubernetes/OpenShift.
• Work with containerised microservices and cloud-native architectures.
• Implement observability, monitoring, logging, and CI/CD integration.

Skills

REST APIs

FastAPI

Flask

Git

CI/CD

Prompt Engineering

Vector Databases

Embeddings

Semantic Search

AI Evaluation

Guardrails

LangChain

LangGraph

Semantic Kernel

Docker

OpenShift

Telecom domain

Knowledge Graphs

AI-driven RCA

Observability

Multi-Agent AI

Tools

Python

FastAPI/Flask

Kubernetes/OpenShift

Job description

Role Summary

• Design and build AI-powered Service Assurance solutions for telecom operations.
• Develop LLM, RAG, and Agentic AI capabilities to automate troubleshooting, RCA, and operational workflows.
• Build production-grade Python services and AI platforms that support autonomous network operations.

Key Responsibilities

AI / GenAI

• Design and develop LLM-powered operational assistants and copilots.
• Build and optimise RAG pipelines using enterprise knowledge, alarms, KPIs, logs, tickets, and documentation.
• Develop Agentic AI workflows for RCA, alarm analysis, incident triage, and recommendation generation.
• Implement prompt engineering, embeddings, semantic search, and response evaluation.
• Improve AI accuracy, grounding, and explainability.

Python Engineering

• Develop scalable backend services using Python.
• Build APIs and microservices using FastAPI/Flask.
• Create automation pipelines for data ingestion, analysis, and AI orchestration.
• Deliver production-quality, maintainable, and testable code.
• Develop and deploy applications on Kubernetes/OpenShift.
• Work with containerised microservices and cloud-native architectures.
• Implement observability, monitoring, logging, and CI/CD integration.

Telecom Service Assurance

• Apply AI to alarm correlation, KPI analytics, RCA, and incident management.
• Work with OSS, FM/PM systems, topology, inventory, alarms, logs, and telemetry data.
• Collaborate with telecom SMEs to convert domain knowledge into AI playbooks and workflows.

Job Requirements

Experience

• 4-8 years of software/platform engineering experience.
• 1-3 years of hands-on experience in GenAI, LLM, RAG, AI Assistants, or Agentic AI solutions.

Mandatory Skills

Programming

• REST APIs
• FastAPI / Flask
• Git & CI/CD

AI / LLM

• Prompt Engineering
• Vector Databases
• Embeddings
• Semantic Search
• AI Evaluation & Guardrails

AI Frameworks

• LangChain
• LangGraph
• Semantic Kernel
• Docker
• OpenShift

Preferred Skills

• Telecom domain knowledge (4G/5G/IMS/OSS).
• Knowledge Graphs.
• AI-driven root cause analysis.
• AIOps and observability platforms.
• Multi-Agent AI systems.
• Incident management and service assurance.

Ideal Candidate

• Strong Python developer with hands-on LLM/RAG implementation experience.
• Has built real-world AI copilots, chatbots, or knowledge assistants.
• Can design end-to-end AI solutions rather than only consume AI APIs.
• Comfortable handling telecom operational data such as alarms, logs, KPIs, and tickets.
• Able to mentor junior engineers and drive technical architecture decisions.