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 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.
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.
