AI Engineering Architect

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