AI Architect

Role Summary

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 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 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.

What You Will Work On

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%.
  

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.