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Generative AI Engineer

Questhiring

2 - 5 years

Gurugram, Ambala

Posted: 17/06/2026

Job Description

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 stable 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 use case.

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, langraph etc with hands on experience)

-Eval generation and their importance

-Tokens usage and optimisations.

-Model/mcp gateway

Ideal profile:

Strong 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 patterns with third party AI providers (OpenAI,

Anthropic, etc.) and vector DB

Hand-on & good understanding of atleast one agentic framework like Langgraph.

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