Role:AI Engineer role(Agentic)

AI Engineer (production / apps focus):
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 inhouse 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, circuitbreakers, and rollback/versioning of models and prompts.
Collaborate with Data Scientists who own model training/finetuning and evaluation design; productionize their outputs as reliable APIs/workflows.
Implement logging, feedback capture, and lightweight online evaluation hooks to measure quality of AI features over time.
Ensure safety, security, and compliance for AI features: prompt injection defenses, PII handling, abuse/hallucination controls, and audit trail.
Contribute to internal AI tooling:



SDKs, templates, and reusable components to accelerate future AI usecase.

Skills Required:
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,langraphetc with hands on experience)
-Eval generation and their importance
-Tokens usage and optimisations.
-Model/mcpgateway

Ideal profile:
Robust software engineering in Python (and one of Node/Java/Go), REST/gRPC APIs, queues, and microservices on cloud infra.
Handson 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 patt

📌 Ai Engineer Gurugram
🏢 Deutsche Telekom Digital Labs
📍 Gurugram