AI Engineer - 2

Deutsche Telekom Digital Labs; Gurugram District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Deutsche Telekom Digital Labs in Gurugram, India, is seeking an AI Engineer-2 to design, build and deploy scalable AI applications using Python, LLMs and RAG pipelines. You will implement agentic AI features and integrate open AI platforms while collaborating with Product, Backend and DevOps teams.

The ideal candidate has 3.6–5 years of software development experience, hands-on Python, experience with AI agents and vector databases, and a solid understanding of REST APIs and scalable system

Qualifications

• 3.6–5 years of software development experience with strong exposure to AI applications.
• Strong hands-on experience with Python (Mandatory).
• Experience building Generative AI and Agentic AI applications.
• Hands-on experience with LLMs, Prompt Engineering, and RAG architectures.
• Experience with AI agent frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen.
• Experience working with Vector Databases such as Pinecone, Chroma, Weaviate, or pgvector.
• Strong understanding of REST APIs and backend development.
• Good problem-solving skills and understanding of scalable system design.

Responsibilities

• Design, develop, and deploy AI-powered applications using Python.
• Build and optimise Agentic AI solutions and autonomous AI workflows.
• Develop Retrieval-Augmented Generation (RAG) pipelines for enterprise use cases.
• Integrate Large Language Models (OpenAI, Anthropic, or similar) into business applications.
• Develop AI agents using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen.
• Optimise AI models for performance, scalability, and reliability.
• Collaborate with Product, Backend, and DevOps teams to build production-ready AI solutions.
• Monitor, evaluate, and continuously improve AI system performance.

Skills

Python

Generative AI

Agentic AI

LLMs

RAG architectures

LangChain

LangGraph

LlamaIndex

CrewAI

AutoGen

Vector databases

Pinecone

Chroma

Weaviate

pgvector

REST APIs

Backend development

Scalable system design

FastAPI

Flask

Django

AI observability tools

Tools

OpenAI

Anthropic

Job description

We are looking for a passionate AI Engineer-2 with strong expertise in Python, Generative AI, and Agentic AI to build intelligent, scalable AI applications. The ideal candidate should have hands‑on experience developing AI-powered solutions using Large Language Models (LLMs), RAG architectures, and AI agent frameworks while collaborating with cross‑functional teams to deliver enterprise‑grade AI products.

Responsibilities

• Design, develop, and deploy AI-powered applications using Python.
• Build and optimise Agentic AI solutions and autonomous AI workflows.
• Develop Retrieval-Augmented Generation (RAG) pipelines for enterprise use cases.
• Integrate Large Language Models (OpenAI, Anthropic, or similar) into business applications.
• Develop AI agents using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen.
• Optimise AI models for performance, scalability, and reliability.
• Collaborate with Product, Backend, and DevOps teams to build production-ready AI solutions.
• Monitor, evaluate, and continuously improve AI system performance.

Requirements

• 3.6-5 years of software development experience with strong exposure to AI applications.
• Strong hands‑on experience with Python (Mandatory).
• Experience building Generative AI and Agentic AI applications.
• Hands‑on experience with LLMs, Prompt Engineering, and RAG architectures.
• Experience with AI agent frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen.
• Experience working with Vector Databases such as Pinecone, Chroma, Weaviate, or pgvector.
• Strong understanding of REST APIs and backend development.
• Good problem‑solving skills and understanding of scalable system design.

Preferred Skills

• Experience with FastAPI, Flask, or Django.
• Knowledge of Docker, Kubernetes, and CI/CD pipelines.
• Experience with AWS, Azure, or GCP cloud platforms.
• Familiarity with MLOps, LLMOps, and AI observability tools.
• Strong communication, collaboration, and analytical skills.