Cloud/AI Architect Agentic AI

This role will focus on architecting scalable AI solutions using AWS Agent Core, Google Cloud Platform Vertex AI, and other cloud-native AI services., Architect and implement Agentic AI pipelines involving multi-step reasoning, tool usage, orchestration, and autonomous workflows.

Build enterprise-grade RAG (Retrieval-Augmented Generation) systems, including data ingestion, chunking strategies, embedding pipelines, vector stores, and retrieval optimization.

Define cloud architecture patterns for AI workloads covering compute, storage, networking, security, scalability, and cost optimization.

Evaluate and select LLM providers, AI orchestration frameworks, and vector databases based on enterprise requirements.

Work with technologies/frameworks such as AWS Agent Core, Google Cloud Platform Vertex AI, LangChain, LangGraph, CrewAI, Bedrock Agents, and equivalent platforms.

Work with vector databases and search technologies such as OpenSearch, Pinecone, pgvector, or equivalent solutions.

Collaborate with Data Engineering, Security, Cloud, Platform, and Engineering teams to ensure enterprise AI solutions meet scalability, reliability, security, and compliance standards.

Develop reusable architecture blueprints, reference architectures, and implementations for Agentic AI and RAG use cases.

Provide technical leadership and mentorship to engineering teams implementing AI solutions.

Evaluate emerging cloud AI services and recommend adoption where they provide measurable business value.

Translate business requirements into scalable, secure, and production-ready AI architectures.

Experience designing and implementing Agentic AI / Autonomous AI systems

Strong experience with AWS and/or Google Cloud Platform cloud architecture

Hands-on experience with RAG architecture and enterprise knowledge retrieval

Experience with LLMs, prompt engineering, embeddings, vector databases, and AI orchestration

Experience with LangChain / LangGraph / CrewAI / Bedrock Agents / Vertex AI or equivalent frameworks

Experience designing enterprise AI infrastructure covering security, networking, compute, storage, scalability, and cost optimization

Experience establishing Agentic AI governance frameworks, including guardrails, audit trails, autonomy limits, and responsible AI controls

Exposure to multiple LLM providers and the ability to determine which model/provider is appropriate for different use cases

Experience building reusable AI platforms, infrastructure patterns, or architecture frameworks adopted across multiple teams

Strong stakeholder management, communication, architecture, and technical leadership skills