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Python LLM + Gen Ai Developer

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Hybrid

Python LLM + Gen Ai Developer

Infosys

Python LLM + Gen Ai Developer

Skills

Google Cloud PlatformDockerPythonMachine LearningAIAWSKubernetesAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

11 August 2026

What they need


We are seeking a skilled Python LLM / Generative AI Developer to design, develop, and deploy AI-powered applications leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and modern GenAI frameworks. You will contribute to feature development, optimize model performance, integrate AI capabilities into enterprise applications, and collaborate with cross-functional teams to deliver innovative AI solutions.

  • Design and develop production-grade Generative AI applications using Python and Large Language Models (LLMs).
  • Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge sources.
  • Develop intelligent AI solutions leveraging frameworks such as LangChain, LlamaIndex, Semantic Kernel, and Azure OpenAI.
  • Integrate external APIs, enterprise systems, and data sources to enhance AI-driven workflows.
  • Implement prompt engineering, model evaluation, and response optimization techniques to improve accuracy and relevance.
  • Develop scalable backend services and APIs to support GenAI applications in production environments.
  • Collaborate with product, engineering, and business teams to embed AI capabilities into enterprise workflows.
  • Contribute to code reviews, technical documentation, and engineering best practices.
  • Strong Python programming with hands-on experience in AI/ML and Generative AI frameworks.
  • Working knowledge of Large Language Models (LLMs), prompt engineering, and model fine-tuning techniques.
  • Practical experience with LangChain, LlamaIndex, Semantic Kernel, OpenAI, Azure OpenAI, or similar GenAI frameworks.
  • Experience with vector databases, embeddings, retrieval systems, and RAG architectures.
  • Understanding of responsible AI practices, model evaluation, guardrails, and AI governance.

Good to Have

  • Exposure to MLOps/LLMOps practices including model versioning, experiment tracking, and deployment automation.
  • Knowledge of Docker, Kubernetes, CI/CD pipelines, and cloud-native application development.
  • Experience with cloud AI services on Azure, AWS, or GCP.
  • Familiarity with monitoring, observability, and performance optimization of AI applications in production.

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