Responsibilities

- Build and maintain scalable backend services using modern technologies such as Python, Node.js, Java, or Go.
- Design and implement APIs that expose AI/LLM-powered capabilities to web, mobile, and enterprise applications.
- Integrate production-grade LLM APIs (OpenAI, Anthropic, Gemini, Azure OpenAI, etc.) into backend systems and workflows.
- Build and manage Retrieval-Augmented Generation (RAG) pipelines including ingestion, chunking, embedding, indexing, retrieval, reranking, and grounding.
- Design and maintain enterprise knowledge bases optimized for LLM and agent consumption.
- Build agentic workflows and multi-step reasoning systems using frameworks such as LangGraph, CrewAI, AutoGen, or equivalent.
- Design AI-enabled automation flows using tools such as n8n, Temporal, Airflow, or similar orchestration platforms.
- Build internal AI-powered developer productivity tools including code assistants, automated documentation generators, test generation systems, release-note generators, and incident-analysis agents.
- Handle LLM operational concerns including prompt management, context engineering, latency optimization, retries, fallback strategies, caching, observability, and cost optimization.
- Work with vector databases and search platforms such as Pinecone, Weaviate, Qdrant, Elasticsearch, or FAISS.
- Implement secure tool integrations and MCP (Model Context Protocol)-based workflows connecting APIs, databases, and enterprise systems to AI agents.
- Design monitoring and evaluation pipelines for AI systems including tracing, prompt/version tracking, hallucination analysis, token/cost monitoring, and performance evaluation.
- Collaborate closely with frontend, mobile, DevOps, QA, security, and product teams in a structured engineering setting.




- Contribute reusable SDKs, internal frameworks, and shared AI platform components.

Skills Required

Backend Engineering

- Strong backend development experience in Python, Node.js, Java, or Go
- REST / GraphQL API design and development
- SQL and NoSQL databases
- Distributed systems and asynchronous processing
- Queues and event-driven architectures (Kafka, RabbitMQ, Pub/Sub, etc.)

AI / LLM Engineering

- Production experience integrating OpenAI, Anthropic, Gemini, or equivalent LLM APIs
- Prompt engineering and context management
- RAG architecture and retrieval pipelines
- Knowledge base construction for LLM systems
- Embedding strategies:
- Dense embeddings
- Sparse retrieval
- Hybrid search
- Reranking pipelines
- Multilingual/domain-specific embeddings

- Vector databases and semantic search systems

Agentic AI & Workflow Orchestration

- LangGraph / CrewAI / AutoGen or similar agent frameworks
- n8n / Temporal / Airflow or equivalent orchestration systems
- MCP (Model Context Protocol) awareness and tool integration patterns
- Agent memory, tool-calling, and workflow design fundamentals

Observability & Reliability

- AI observability and tracing tools such as LangSmith, Langfuse, MLflow, OpenTelemetry, Grafana, Datadog, or equivalent
- Token, latency, retry, and cost monitoring
- Evaluation pipelines for prompts and agent workflows
- Logging, metrics,



tracing, and production debugging

Cloud & DevOps

- AWS / GCP / Azure
- Docker and containerized deployments
- CI/CD pipelines
- Automated testing and release workflows

Ideal Profile

1. 4–7 years of backend engineering experience with strong system design fundamentals.
2. Hands-on experience building AI-powered backend systems, RAG pipelines, or agentic workflows in production environments.
3. Strong understanding of LLM limitations, hallucination mitigation, grounding strategies, and cost-performance tradeoffs.
4. Experience designing scalable AI infrastructure and enterprise-grade APIs.
5. Familiarity with agent monitoring, AI evaluation frameworks, and workflow orchestration platforms.
6. Strong debugging, performance optimization, and problem-solving skills.
7. Experience working in cross-functional product and engineering teams.

Deutsche Telekom is one of the world's leading integrated telecommunications companies, with some 252 million mobile customers, 25 million fixed-network lines, and 22 million broadband lines. Deutsche Telekom is present in more than 50 countries. With a staff of some 200,000 (Dec 31, 2023) employees throughout the world, we generated revenue of 111,9 billion Euros in the 2023 financial year. As of 2023, It is rated as the 9th most valuable brand in the world.

Deutsche Telekom Digital Labs is the digital powerhouse that fuels Deutsche Telekom’s vision to be a “Leading Digital Telco”. We create compelling digital propositions for the worlds’ largest Telekom giant by building scalable, agile and innovative platforms to provide the best of digital experience to Deutsche Telekom customers across Europe and US.

📌 AI Engineer (Gurugram)
🏢 Deutsche Telekom Digital Labs
📍 Gurugram