AI Engineer

Job Code: DTDLPL-81045

Gurugram, Haryana, India

Expires on 29/07/2026

Required Experience

2 - 4 Years

Skills

Langchain,

Langraph,

Python

Job 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

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.