Lead AI Engineer

Deutsche Telekom Digital Labs; Gurugram District; Hybrid; INR 4,000,000 - 6,000,000; Full time

Job summary

Deutsche Telekom Digital Labs seeks an experienced Lead AI Engineer to design and develop production-grade generative AI, LLM, RAG, and agentic AI solutions. You will mentor teams, drive technical architecture, and deliver scalable AI systems for enterprise use cases.

You will work with Python, FastAPI, and cloud platforms, and integrate diverse LLM providers while advancing MLOps/LLMOps practices and security.

Qualifications

• 8-10 years of software/AI engineering experience.
• Strong Python programming experience.
• Hands-on generative AI/LLM experience.
• Strong RAG architecture and implementation experience.
• Experience with Agentic AI / AI Agents / Multi-Agent Systems.
• Knowledge of vector databases and modern cloud platforms.
• Experience with REST APIs, Docker, Kubernetes and microservices.
• Familiarity with MLOps / LLMOps concepts.
• Understanding of AI system architecture and scalability.

Responsibilities

• Design and develop scalable GenAI and LLM-powered apps for enterprise use cases.
• Architect and implement advanced RAG pipelines including embedding, retrieval and re-ranking.
• Build agentic AI and multi-agent systems using LangChain, LangGraph, AutoGen or Semantic Kernel.
• Integrate multiple LLM providers (OpenAI, Azure OpenAI, Claude, Gemini) and open-source models.
• Develop AI services and microservices with Python and FastAPI.
• Work with vector databases (FAISS, Pinecone, Weaviate, Milvus, Azure AI Search).
• Implement prompt engineering, function/tool calls, memory, and orchestration of agents.
• Develop LLM evaluation/monitoring to improve accuracy, safety, and reliability.
• Deploy AI solutions on Azure/AWS/GCP with Docker and Kubernetes.
• Establish MLOps/LLMOps pipelines, CI/CD, monitoring and production deployment practices.
• Mentor engineers and provide technical leadership across AI initiatives.

Skills

Python

LLMs

RAG architecture

Agentic AI

LangChain/LangGraph

Vector databases

Cloud platforms

MLOps

Docker/Kubernetes

Mentoring

Education

Bachelor's degree in CS or related field

Tools

LangChain

LangGraph

OpenAI

Azure OpenAI

FastAPI

Job description

We are looking for an experienced Lead AI Engineer to lead the design and development of production-grade generative AI, LLM, RAG, and agentic AI solutions.

The ideal candidate should have strong hands-on experience in Python, LLMs, RAG architecture, AI agents, LangChain/LangGraph, vector databases, cloud, and MLOps, along with the ability to design scalable AI systems and mentor engineering teams.

Responsibilities:

• Design and develop scalable GenAI and LLM-powered applications for enterprise use cases.
• Architect and implement advanced RAG pipelines, including document processing, embeddings, retrieval, re-ranking, and evaluation.
• Build agentic AI and multi-agent systems using frameworks such as LangChain, LangGraph, AutoGen, or Semantic Kernel.
• Integrate LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, or open-source LLMs.
• Develop AI services and microservices using Python and FastAPI.
• Work with vector databases such as FAISS, Pinecone, Weaviate, Milvus, or Azure AI Search.
• Implement prompt engineering, function/tool calling, structured outputs, memory, and agent orchestration.
• Build and maintain LLM evaluation and monitoring frameworks to improve accuracy, relevance, safety, and reliability.
• Deploy AI solutions on Azure/AWS/GCP using Docker and Kubernetes.
• Establish and improve MLOps/LLMOps pipelines, CI/CD, model monitoring, and production deployment practices.
• Work on AI security, responsible AI, guardrails, prompt injection prevention, and LLM safety.
• Drive technical architecture, code reviews, engineering best practices, and system design.
• Mentor engineers and provide technical leadership across AI initiatives.

Requirements:

• 8-10 years of software/AI engineering experience.
• Strong Python programming.
• Hands-on generative AI/LLM experience.
• Strong RAG architecture and implementation experience.
• Experience with Agentic AI / AI Agents / Multi-Agent Systems.
• LangChain / LangGraph or equivalent agent frameworks.
• Experience with Vector Databases.
• LLM APIs such as OpenAI / Azure OpenAI / Claude / Gemini.
• Experience in FastAPI / REST APIs / Microservices, Docker, and Kubernetes.
• Experience with at least one major cloud platform: Azure, AWS, or GCP.
• Understanding of MLOps / LLMOps.
• Strong knowledge of AI system architecture and scalability.
• Good technical leadership and mentoring experience.
• Good to have: LlamaIndex, AutoGen, Semantic Kernel, Hugging Face Transformers, PyTorch / TensorFlow, MLflow / Kubeflow, Terraform and CI/CD, LLM evaluation and observability tools, AI red teaming / AI security, OWASP LLM Top 10 / NIST AI RMF, Fine-tuning / LoRA / QLoRA.
• Knowledge graphs and hybrid search.