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Artificial Intelligence Engineer

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Onsite

Artificial Intelligence Engineer

Infosys

Artificial Intelligence Engineer

Skills

Google Cloud PlatformSQL QueriesDeep LearningDockerPythonKnowledge ManagementMicroservicesMachine LearningAIAWSKubernetesNLP / Natural Language ProcessingAzure

11 days ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

12 October 2026

What they need


Role & responsibilities

AI Solution Design & Architecture

  • Design end-to-end AI/GenAI architectures and technical solutions.
  • Build scalable and secure AI platforms leveraging LLMs and Agentic AI frameworks.
  • Create High-Level Design (HLD) and Low-Level Design (LLD) documents for AI applications.
  • Define AI governance, security, and responsible AI best practices.

Generative AI Development

  • Develop and deploy applications using Large Language Models (LLMs).
  • Design and implement Retrieval-Augmented Generation (RAG) solutions.
  • Fine-tune, optimize, and evaluate foundation models for business use cases.
  • Build AI-powered chatbots, virtual assistants, knowledge management, and automation solutions.
  • Develop prompt engineering strategies and model evaluation frameworks.

Agentic AI & Multi-Agent Systems

  • Design and implement autonomous AI agents and multi-agent workflows.
  • Build agent orchestration, tool integration, memory management, and reasoning capabilities.
  • Develop intelligent systems capable of planning, decision-making, and task automation.
  • Integrate AI agents with enterprise applications, APIs, and data sources.

Development & Integration

  • Develop AI applications using Python and modern AI frameworks.
  • Build APIs and microservices for AI model deployment and integration.
  • Implement scalable AI pipelines and production-grade MLOps processes.
  • Optimize model performance, latency, and cost efficiency.

Required Skills

  • Programming: Python, SQL
  • Generative AI: LLMs, Prompt Engineering, RAG, Fine-Tuning
  • Agentic AI: AI Agents, Multi-Agent Systems, Tool Calling, Agent Orchestration
  • Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen
  • AI Models: OpenAI, Claude, Gemini, Open Source LLMs
  • Machine Learning: NLP, Deep Learning, Model Evaluation
  • Cloud: AWS, Azure, or GCP AI Services
  • MLOps: Docker, Kubernetes, CI/CD, Model Deployment
  • Databases: Vector Databases (Pinecone, Chroma, Weaviate, FAISS), SQL/NoSQL
  • APIs & Integration: REST APIs, Microservices

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