Artificial Intelligence Engineer

Questhiring; Gurugram District; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Questhiring is seeking an AI Engineer to design and ship AI-powered features such as LLMs, RAG, and agents into existing services. You will partner with backend, frontend, and data science teams to deploy robust models in microservices and user flows.

You will own RAG pipelines, context assembly, tool integration, and guardrails, while ensuring latency, throughput, and cost targets are met. Collaboration with data scientists to productionize outputs as APIs will be key.

Qualifications

• Hands-on software engineering experience with Python and at least one modern language.
• Strong understanding of AI features, prompts, and evaluation metrics.
• Experience with LLMs, embeddings, and model integration in production.
• Familiarity with Langchain, Langraph, and vector databases.
• Ability to design robust AI pipelines, guardrails, and observability.

Responsibilities

• Design and ship AI-powered features (LLMs, RAG, agents) into existing services.
• Integrate off-the-shelf and in-house models into microservices and user flows.
• Build RAG pipelines, context assembly, tools integration, and fallbacks.
• Own AI service reliability: latency, throughput, cost, observability.
• Collaborate with data scientists to productionize model outputs as APIs.
• Implement logging, evaluation hooks, and safety/audit tooling.

Skills

RAG

GenAI & LLM finetuning

Prompt engineering

Multi-Agent framework

Eval generation

Tokens optimization

Python

REST/gRPC APIs

Microservices

OpenAI integration

Vector DB

Tools

Langchain

Langraph

Vector DB

OpenAI API

Job description

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.