AI Architect (AWS & Agentic AI)

About AI Labb

AI Labb is the Data & AI division of TheAppLabb, a technology innovation firm that has launched 750+ applications over the past 18 years, for global brands including Suncor, Loblaw, Petro Canada, RBC, Porter Airlines, Chatters, David Yurman, Meridian, Empire Life, First Canadian Title, Gateway Casinos, Canadian Standards Association, among others. We bridge the gap between AI's promise and business reality, delivering pragmatic AI solutions that create measurable business outcomes rather than theoretical possibilities.

Our approach is business-first: we start with client objectives and work backward to ensure every AI initiative delivers measurable ROI. We are looking for builders who share this philosophy.

Role Summary

We are looking for a hands-on AI Architect who can code, architect, and deploy. You must be able to take a concept from a whiteboard to a working Proof of Concept independently. This role combines deep AWS Cloud Architecture expertise with modern Agentic AI patterns to build autonomous systems that solve real business problems.

You will work directly with clients across retail, financial services, healthcare, and manufacturing, translating their business challenges into working AI solutions. You will also play a key role in growing the AI Labb team by technically vetting and onboarding future engineers.

Core Responsibilities

Hands-On Agentic AI Development

You will build PoCs independently without relying on a dev team for the initial build. 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. Our clients expect working demonstrations, not slide decks.

AWS Cloud Architecture (PaaS Focus)

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.

Client Delivery & Solution Design

You will participate in AI Discovery engagements to identify high-value opportunities within client organizations. 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 new hires within the AI Labb 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.

Multi-Cloud & Integration

You will integrate AI services into existing enterprise workflows and data pipelines. You will maintain operational knowledge of Azure and GCP to support client-specific multi-cloud requirements.

Must-Have Qualifications

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. We expect you to have built cloud infrastructure before moving into AI.

AI Stack: Proven experience with Amazon Bedrock, SageMaker, and Vector Databases such as Pinecone or OpenSearch.

Agentic Experience: Demonstrated ability to build Agents that utilize tools and function calling. We are not looking for people who have only built simple chatbots.

Consulting Mindset: Ability to communicate technical concepts to business stakeholders and translate business problems into technical solutions.

Nice-to-Have Qualifications

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.

MLOps: Familiarity with MLflow, Kubeflow, or Weights & Biases for model lifecycle management.

What You Will Work On

AI Labb delivers solutions across several domains. Here are examples of the types of projects you would contribute to:

Building multi-agent systems that reduce manual decision-making by 30% and improve response times by 40%.

Implementing AI-driven quality monitoring for manufacturing clients that reduces batch rejections by 25%.

Developing hyper-personalization engines for retail clients that increase digital conversion rates by 20-27%.

Creating data pipelines and AI infrastructure that enable new AI initiatives while reducing data preparation time by 60%.

Why AI Labb

Work with Enterprise Clients: Our client roster includes major brands across manufacturing, retail, financial services, and healthcare.

Business-First Philosophy: We cut through AI hype to deliver real value. Every project starts with measurable objectives.

Direct Leadership Access: You will work closely with our VP of AI Engineering, Principal AI Solutions Architect, and Chief AI Strategy Officer.

Growth Opportunity: As an early member of a scaling team, you will shape our technical direction and build the team around you.

Partnership Ecosystem: Access to AWS, Azure, Google Cloud, Snowflake, Databricks, and leading AI/ML platforms.

How to Apply

Send your resume along with one of the following: a link to a GitHub repo showing an agentic AI project you have built, a brief write-up describing a complex AI system you have architected, or a demo video of a PoC you have created.

We want to see evidence that you can build, not just design.