Placement Role - Cloud /AI Architect with RAG and Agentic AI with AWS/ Google Cloud Platform Exp (Hybrid) - CT

Infinity Tech Group; Stamford (CT); Hybrid; USD 160,000 - 210,000; Full time

Job summary

Infinity Tech Group in Stamford, CT is seeking a Cloud/AI Architect to design and build enterprise-grade agentic AI systems. You will architect scalable AI solutions using AWS Agent Core or GCP AI services, shaping the technical strategy for autonomous agents and knowledge retrieval.

The role emphasizes governance, guardrails, audit trails, and compliance, with exposure to multiple LLM providers. You will mentor engineers, create reusable blueprints, and partner with stakeholders to translate

Responsibilities

• Architect and implement agentic AI pipelines (multi-step reasoning, tool use, orchestration) using AWS Agent Core, Google Cloud Platform Vertex AI, or equivalent frameworks
• Design and build enterprise RAG systems, including data ingestion, chunking strategies, embedding pipelines, vector store integration, and retrieval optimization
• Define cloud architecture patterns for AI workloads compute, storage, networking, security, and cost optimization
• Evaluate and select appropriate LLM providers, orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, Bedrock Agents), and vector databases (e.g., OpenSearch, Pinecone, pgvector)
• Collaborate with data engineering, security, and platform teams to ensure AI solutions meet enterprise standards for scalability, reliability, and compliance
• Build reusable architectural blueprints and reference implementations for agentic AI and RAG use cases across business units
• Provide technical leadership and mentorship to engineering teams implementing AI solutions
• Stay current with evolving cloud AI services and recommend adoption where it drives business value
• Partner with stakeholders to translate business requirements into scalable, production-ready AI architecture
• Experience standing up agentic AI governance frameworks guardrails, audit trails, autonomy limits in a regulated or enterprise environment.
• Exposure to multiple LLM providers and a clear point of view on when to use which.
• Prior work building reusable platform or infrastructure patterns that got adopted org-wide, not just within one team.

Job description

Placement Role: Cloud /AI Architect with RAG and Agentic AI with AWS Experience (Hybrid 1-2 days Onsite/ Week)

(Direct Client)

Location: Stamford, CT

Duration: 12-18+ Months

Cloud/AI Architect to design and build enterprise-grade agentic AI systems. This role will focus on architecting scalable AI solutions using AWS Agent Core or Google Cloud Platform-native AI services, driving the technical strategy for how our organization builds, deploys, and governs autonomous AI agents and knowledge retrieval systems.

Key Responsibilities

• Architect and implement agentic AI pipelines (multi-step reasoning, tool use, orchestration) using AWS Agent Core, Google Cloud Platform Vertex AI, or equivalent frameworks
• Design and build enterprise RAG systems, including data ingestion, chunking strategies, embedding pipelines, vector store integration, and retrieval optimization
• Define cloud architecture patterns for AI workloads compute, storage, networking, security, and cost optimization
• Evaluate and select appropriate LLM providers, orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, Bedrock Agents), and vector databases (e.g., OpenSearch, Pinecone, pgvector)
• Collaborate with data engineering, security, and platform teams to ensure AI solutions meet enterprise standards for scalability, reliability, and compliance
• Build reusable architectural blueprints and reference implementations for agentic AI and RAG use cases across business units
• Provide technical leadership and mentorship to engineering teams implementing AI solutions
• Stay current with evolving cloud AI services and recommend adoption where it drives business value
• Partner with stakeholders to translate business requirements into scalable, production-ready AI architecture
• Experience standing up agentic AI governance frameworks guardrails, audit trails, autonomy limits in a regulated or enterprise environment.
• Exposure to multiple LLM providers and a clear point of view on when to use which.
• Prior work building reusable platform or infrastructure patterns that got adopted org-wide, not just within one team.

Why This Role

• Direct influence over how AI gets built across the company not one team's tool, but the standard everyone else builds on.
• A genuinely open architecture problem, not a maintenance job with a fixed answer key.
• Room to grow into the person others come to for how agentic AI should be done here.