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Artificial Intelligence Engineer

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Onsite

Artificial Intelligence Engineer

Incedo

Artificial Intelligence Engineer

Skills

DockerPythonMicroservicesJenkinsMachine LearningAIAWSElasticsearchKubernetesAzure

1 month ago

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Important information


Contract type:

Permanent

Salary:

₹500,000 – ₹1,500,000 / year

Location:

Gurugram, Haryana, India

Work mode:

Onsite

Published on:

10 September 2026

What they need


Python Dev

AI Engineer 3–7 Years

Location: Gurgaon (First Preference) / Hyderabad
Work Model: 4 Days WFO + 1 Day WFH
Joining: Immediate Joiners Preferred

Role Overview

We are looking for a Senior AI Engineer with 5–7 years of experience with strong hands-on expertise in Generative AI, LLMs, RAG, Prompt Engineering, LangChain and LangGraph. The candidate should have strong Python programming and the ability to design and implement production-ready AI solutions.

Primary Skills

  • Strong Python programming, logical reasoning and problem-solving
  • Strong understanding of LLMs and Generative AI
  • RAG architecture and implementation
  • Prompt Engineering
  • Embeddings & Vector Search
  • Hands-on experience with LangChain & LangGraph
  • AI/ML model development, evaluation and optimization
  • Supervised & Unsupervised Learning fundamentals
  • API development and integration
  • Experience building production-grade AI solutions

Good to Have

  • Agentic AI / AI Agents
  • Docker & Kubernetes
  • AWS / Azure
  • Git, Jenkins / CI-CD
  • Microservices
  • Swagger
  • Keycloak
  • Elasticsearch & Kibana
  • Nexus / artifact management
  • Model / Agent monitoring

Key Responsibilities

  • Design and develop scalable Generative AI and LLM-based applications.
  • Build and optimize RAG pipelines, embeddings and vector search solutions.
  • Develop complex AI workflows using LangChain and LangGraph.
  • Design AI agents and agentic workflows where applicable.
  • Develop and integrate APIs for AI/ML applications.
  • Evaluate and optimize LLM/model performance and output quality.
  • Work with engineering teams on deployment, CI/CD and productionization.
  • Contribute to architecture, technical design and best practices for AI solutions.
  • Troubleshoot AI application issues and improve reliability and performance.

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