Azure AI/ML Architect - Lead End-to-End AI Solutions

Tata Consultancy Services Limited; Allentown (NY); On-site; USD 160,000 - 170,000; Full time

Benefits offered by this job

Medical Coverage

Parental Leaves

401K Plan

Performance Bonus

Job summary

Tata Consultancy Services Limited in Allentown, NY seeks a Technical Architect to architect and drive AI/ML solutions on Azure & Databricks. You will lead design sessions, mentor teams, and translate business problems into scalable AI applications.

The role emphasizes end-to-end ML lifecycle, from data pipelines to model governance, with focus on secure, compliant implementations and collaboration with stakeholders.

Qualifications

• Hands-on experience with Azure AI and data services.
• Strong Databricks and MLflow know-how.
• Proficiency in Python and SQL for ML workflows.
• Experience with MLOps and model governance.
• Understanding of data architecture and feature stores.
• Knowledge of containers and API-based model serving.

Responsibilities

• Architect end-to-end AI/ML solutions using Azure and Databricks.
• Lead design sessions and guide scalable AI practices.
• Collaborate with data scientists, engineers, and stakeholders.
• Design and implement MLOps for training, deployment, monitoring.
• Optimize data pipelines and feature engineering with Spark/Delta Lake.
• Ensure data privacy, security, and governance compliance.
• Evaluate emerging AI tech and guide adoption.
• Provide technical leadership and mentorship.

Skills

Azure AI services

Databricks

Python

SQL

ML frameworks

MLOps

Data architecture

Feature stores

Real-time inference

Containerization

API development

Communication

Education

Bachelor of Computer Science

Tools

Azure ML

Azure Synapse

Azure Data Factory

Azure Data Lake

Azure Functions

Databricks

Spark

Delta Lake

MLflow

Docker

Kubernetes

Job description

Tata Consultancy Services Limited in Allentown, NY seeks a Technical Architect to architect and drive AI/ML solutions on Azure & Databricks. You will lead design sessions, mentor teams, and translate business problems into scalable AI applications.

The role emphasizes end-to-end ML lifecycle, from data pipelines to model governance, with focus on secure, compliant implementations and collaboration with stakeholders.