Infosys
Python LLM + Gen Ai Developer
Skills
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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