This role combines deep AWS Cloud Architecture expertise with modern Agentic AI patterns to build autonomous systems that solve real business problems. This means designing and coding AI Agents using AWS Bedrock and frameworks like LangChain or LangGraph, implementing reasoning, planning, and memory modules. You will configure LLMs to interact with external APIs, databases, and enterprise software to execute real-world tasks.

You will design scalable infrastructure using AWS PaaS services including Lambda, Fargate, API Gateway, EventBridge, and Step Functions. You will select and optimize Foundation Models via Amazon Bedrock or SageMaker based on cost, latency, and performance requirements. All architectures must meet strict security, compliance, and cost-optimization standards.

You will translate business requirements into technical architectures that align with our outcome-driven methodology. You will work alongside our AI Strategy and Implementation teams to deliver end-to-end solutions. Team Building & Technical Leadership You will lead technical interviewing, selection, and onboarding for recent hires within the AI workstream.

You will define technical standards and coding guidelines for our growing AI/ML team. You will contribute to knowledge transfer initiatives,



building client capabilities rather than dependencies. You will integrate AI services into existing enterprise workflows and data pipelines.

AWS Certification: Must hold a valid AWS Certified Solutions Architect (Associate or Professional). Hands-On Coding: Strong proficiency in Python. You must be comfortable writing production-grade code, not just managing configurations or reviewing pull requests.

Cloud Background: Strong foundation in traditional Cloud Architecture including networking, IAM, and serverless patterns.

AI Stack: Proven experience with Amazon Bedrock, SageMaker, and Vector Databases such as Pinecone or OpenSearch. Ability to communicate technical concepts to business stakeholders and translate business problems into technical solutions.

Data Background: High-level understanding of Data Warehouses (Snowflake, Redshift) and Data Lakes to understand data lineage and retrieval strategies. This helps when working with our Data Foundation services.

DevOps: Experience with CI/CD pipelines and Infrastructure as Code using Terraform or CDK.

RAG Implementation: Experience building production RAG systems with enterprise document collections.

📌 DevOps Architect - DevOps Cloud Platform (Toronto)
🏢 Zohorecruit
📍 Toronto