AI Architect – Retail / GenAI

BlackCube Labs; Duluth (GA); Hybrid; USD 76,000 - 83,000; Full time

Job summary

Zensar is seeking an AI Architect for Retail / GenAI with 12+ years in IT and 5+ years in AI/ML architecture. The role requires strong Retail domain experience and enterprise GenAI implementations. You will lead architecture, oversee production deployments, and collaborate with stakeholders across the business.

The position is Hybrid in Duluth, GA, offering $55-$60/hr and flexibility for short-notice joining. Prior experience with LLMs, MLOps, and vector data stores is essential.

Qualifications

• 12+ years IT with 5+ years AI/ML Architecture
• Strong Retail domain experience – Mandatory
• Enterprise GenAI architecture & production implementations
• Proficient in LLMs, RAG, Agentic AI, NLP, ML/DL
• Proficient in Python, SQL, Spark and data engineering
• GCP and Google Vertex AI expertise
• Experience with MLOps (MLflow / Kubeflow / Databricks)
• Familiarity with vector databases (Pinecone / Weaviate / ChromaDB)
• AI governance and security, model monitoring

Skills

AI/ML Architecture

Retail domain experience

LLMs / GenAI / NLP

Python / SQL / Spark

GCP / Vertex AI

MLOps (MLflow / Kubeflow / Databricks)

Vector databases (Pinecone / Weaviate)

AI governance / Responsible AI

Executive stakeholder management

Production AI architecture

Tools

Python

SQL

Spark

Google Vertex AI

MLflow

Kubeflow

Databricks

Pinecone

Weaviate

ChromaDB

Job description

Role: AI Architect – Retail / GenAI

Client: Zensar

Location: -Duluth, GA – Hybrid (3 Days Onsite)

Rate: $55-$60/hr

Visa Category: H1B only (C2C allowed) (No GC and USC on this)

Experience: 12+ Years

Type: Full-Time & Contract

Joining: Short Notice Preferred

Key Skills

• 12+ years overall IT with5+ years AI/ML Architecture
• Strong Retail domain experience – Mandatory
• Enterprise AI/GenAI architecture & production implementations
• LLMs,RAG, Agentic AI, NLP, ML/DL
• Python, SQL, Spark & modern data engineering
• GCP & Google Vertex AI
• Enterprise AI platforms
• MLOps – MLflow, Kubeflow, Databricks or equivalent
• Vector databases – Pinecone, Weaviate, ChromaDB or equivalent
• AI governance, responsible AI, security & model monitoring
• Strong architecture leadership and executive stakeholder management