AI Engineer

DTDL group.; Gurugram District; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

DTDL group. in Gurugram, India is seeking an AI Engineer to build scalable backend services powering AI/LLM capabilities. You will design and implement enterprise-grade APIs, integrate AI APIs, and build RAG pipelines with retrieval, embedding, indexing, and grounding.

You will construct agentic workflows with LangGraph or similar, design AI-enabled automation, and ensure observability, performance, and cost efficiency while collaborating with frontend, DevOps, QA, security, and product teams.

Qualifications

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

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

Skills

Backend Engineering

AI / LLM Engineering

Agentic AI

Observability

Cloud & DevOps

System Design

Tools

Python

Node.js

Java

Go

REST

GraphQL

Job description

#### AI Engineer###### Job Code: DTDLPL-81045Gurugram, Haryana, IndiaExpires on 29/07/2026Required Experience2 - 4 YearsSkillsLangchain,Langraph,Python+ 2 moreJob Description**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 environment.* 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.