Lead AI Engineer

Deutsche Telekom Digital Labs; Delhi; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Deutsche Telekom Digital Labs seeks an AI Engineer to design and ship AI-powered features, integrating LLMs, RAG, and agents into scalable microservices. You will work with data scientists and software teams to productionize models as robust APIs and workflows.

You'll implement safety controls, logging, and evaluation hooks while optimizing latency and cost in a cloud environment. Strong Python and API skills required.

Qualifications

• Hands-on experience shipping at least one AI-powered product to production.
• Strong software engineering in Python and one of Node/Java/Go with REST/gRPC APIs.
• Practical knowledge of LLM concepts: prompts, context engineering, embeddings, and latency/cost trade-offs.
• Solid understanding of integration patterns with OpenAI, Anthropic, etc. and vector DB.

Responsibilities

• Design and ship AI-powered features (LLMs, RAG, agents) into services.
• Integrate off-the-shelf and in-house models into robust microservices and user-facing flows.
• Design RAG and workflow pipelines: retrieval, context assembly, tools integration, guardrails, and fallbacks.
• Own AI service reliability in production: latency, throughput, cost, observability, versioning.
• Collaborate with Data Scientists to productionize outputs as stable APIs/workflows.
• Implement logging, feedback capture, and lightweight online evaluation hooks.
• Ensure safety, security, and compliance for AI features: prompt injection defenses, PII handling, audit trails.
• Contribute to internal AI tooling: SDKs, templates, and reusable components.

Skills

RAG concepts

GenAI / LLM finetuning

Prompt engineering

Multi-Agent frameworks

Evaluation generation

Token usage optimization

Model gateway

Python

Node/Java/Go

REST/gRPC APIs

Cloud microservices

Vector search

Tools

Langchain

Langgraph

Vector DB

OpenAI

Anthropic

Job description

Responsibilities:

• Design and ship AI-powered product features (LLMs, RAG, agents, ML APIs) into our existing services, working closely with backend, frontend, and data science teams.
• Integrate off-the-shelf and in-house models (LLMs, embeddings, ML APIs) into robust microservices and user-facing flows.
• Design and implement RAG and workflow/agent pipelines: retrieval, context assembly, tools integration, guardrails, and fallbacks.
• Own AI service reliability in production: latency, throughput, cost, observability, circuit breakers, and rollback/versioning of models and prompts.
• Collaborate with Data Scientists who own model training/finetuning and evaluation design; productionize their outputs as stable APIs/workflows.
• Implement logging, feedback capture, and lightweight online evaluation hooks to measure the quality of AI features over time.
• Ensure safety, security, and compliance for AI features: prompt injection defenses, PII handling, abuse/hallucination controls, and audit trails.
• Contribute to internal AI tooling: SDKs, templates, and reusable components to accelerate future AI use cases.

Requirements:

• AI Engineer role demands more than AI-based augmentation with in-depth understanding of concepts like: RAG, GenAI, LLM finetuning, Prompt engineering Multi-Agent framework (langchain, langraph etc with hands on experience), Eval generation and their importance, Tokens usage and optimisations., Model/mcp gateway.
• Strong software engineering in Python (and one of Node/Java/Go), REST/gRPC APIs, queues, and microservices on cloud infrastructure.
• Hands-on experience shipping at least one AI powered product to production (e. g., search, recommendations, chatbots, summarization, classification).
• Practical knowledge of LLM concepts: prompts, context engineering, embeddings, vector search, basic evaluation metrics, and latency/cost trade-offs.
• Solid understanding of integration patterns with third-party AI providers (OpenAI, Anthropic, etc. ) and vector DB.
• Hands-on and good understanding of at least one agentic framework like Langgraph.