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Python AI Engineer-Gurgaon

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Hybrid

Python AI Engineer-Gurgaon

Kezan Consulting

Python AI Engineer-Gurgaon

Skills

Google Cloud PlatformSQL QueriesDockerFlaskPythonPyTorchTensorFlowAIAWSKubernetesFastAPIAzure

2 months ago

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Important information


Contract type:

Permanent

Salary:

₹1,200,000 – ₹2,200,000 / year

Location:

Delhi, India

Work mode:

Hybrid

Published on:

31 August 2026

What they need


Python AI Engineer 4+ Years

Location: Gurgaon/Gurugram
Experience: 4+ Years
Work Mode: Hybrid
Employment Type: Full-Time
Joining: Immediate

Job Overview

We are looking for a skilled Python AI Engineer with 4+ years of experience in Python development and hands-on experience in AI/ML, Generative AI, LLM, and RAG-based applications.

Key Responsibilities

  • Develop scalable AI/GenAI applications using Python, FastAPI, and REST APIs.
  • Build and optimize LLM, RAG, and Agentic AI solutions.
  • Work with LangChain, LangGraph, OpenAI APIs, and Hugging Face.
  • Develop AI agents, tool-calling workflows, and multi-agent applications.
  • Implement embeddings, vector search, semantic search, and vector databases such as Pinecone/FAISS.
  • Design data ingestion, preprocessing, retrieval, and evaluation pipelines.
  • Integrate AI solutions with databases, APIs, and enterprise applications.
  • Deploy and maintain production AI applications using Docker and cloud platforms.
  • Optimize AI applications for performance, scalability, reliability, and response quality.

Must-Have Skills

  • 4+ years of strong Python development experience.
  • Hands-on experience with FastAPI/Flask and REST APIs.
  • Strong understanding of LLMs, Generative AI, and RAG.
  • Experience with LangChain / LangGraph.
  • Experience with OpenAI or other LLM APIs.
  • Knowledge of Vector Databases, Embeddings, and Semantic Search.
  • Strong SQL and database fundamentals.
  • Good understanding of Git, Docker, and CI/CD.
  • Experience with AWS/Azure/GCP is preferred.

Good to Have

  • Agentic AI / Multi-Agent Systems
  • RAG evaluation using RAGAS / LangSmith
  • MCP / Tool Calling / Function Calling
  • PyTorch / TensorFlow / Scikit-learn
  • Kubernetes and cloud deployment

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