Key Responsibilities:

- AI Architecture Engineering
- Define and own AI reference architectures for generative AI agentic systems and AI augmented applications
- Architect scalable solutions using LLMs multi agent systems orchestration frameworks and AI pipelines
- Design AI platforms supporting model serving prompt management RAG and workflow orchestration
- Establish architectural standards for performance scalability reliability and cost efficiency
- Platform Engineering Integration
- Build reusable AI components for LLM integration vector search embeddings and inference services
- Enable secure and scalable deployment using Kubernetes serverless platforms and CI CD pipelines
- Integrate AI capabilities into enterprise systems using APIs SDKs and event driven architectures
- Collaborate with QE teams to embed AI into test automation test data generation and intelligent validation

Technical Requirements:

- LLM AND RAG OR Retrieval Augmented Generation AND
- LangChain OR LangGraph OR CrewAI OR AutoGen AND




- Python AND Kubernetes Agentic AI OR Multi Agent Systems OR
- Autonomous Agents OR
- AI Orchestration AWS Bedrock OR Azure OpenAI OR Vertex AI AND
- Kubernetes OR Docker AND
- Microservices OR Cloud Native

Preferred Skills:

Technology->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag),Technology->Open System->Open System- ALL->Python,Technology->AI-AI Engineering->Model Deployment (Kubernetes),Technology->AI-Agentic AI->Agent Engineering,Technology->AI-Agentic AI->Google Cloud – Contact Center AI,Technology->AI-Agentic AI->AgentOps,Technology->AI-AI Hyperscalers->Google Agentic AI Services->GCP,Technology->AI-AI Hyperscalers->AWS Agentic AI Services->AWS Neuron,Technology->AI-AI Hyperscalers->Azure Agentic AI Services->Azure NLP,Technology->AI-AI Engineering->AI/ML Solution Architecture and Design

📌 AI Engineering Architect (India)
🏢 Infosys
📍 India