Senior Director - Transformation Head

Job Description: Sr. Enterprise Architect

Location: PAN India

Level: Senior Director

Experience Level: over all 22 year's experience and in Data, AI, and Platform Engineering

Job Title: Enterprise Architect – AI Training Data Services

Role Overview

We are looking for a hands-on Enterprise Architect with strong technical expertise in data platforms, AI/ML lifecycle, and multi-cloud architecture (AWS, Azure, GCP). This role will define and implement the end-to-end architecture and design for Cognizant’s AI Training Data Service (AITDS) platform, ensuring it is scalable, secure, and optimized for enterprise AI readiness.

This position requires deep technical involvement—from designing architecture blueprints to selecting cloud-native services, partnering with external products and guiding implementation.

Key Responsibilities

• Architecture Design & Implementation
• Define the AITDS platform architecture, including data ingestion, profiling, annotation, validation, governance, and deployment workflows.
• Design modular, API-driven components for integration with enterprise systems and cloud ecosystems.
• Multi-Cloud Expertise
• Select and integrate services from AWS (S3, Glue, SageMaker, Redshift), Azure (Data Factory, Synapse, Cognitive Services), and GCP (BigQuery, Vertex AI, Dataflow).
• Optimize architecture for cost, performance, and scalability across multiple cloud providers.
• Hands-on Technical Leadership
• Work closely with engineering teams to implement architecture using containerization (Docker/Kubernetes), microservices, and serverless technologies.
• Define CI/CD pipelines and DevOps practices for platform deployment.
• Data & AI Readiness
• Architect solutions for high-quality training data generation, annotation, and validation for AI/ML models.
• Ensure compliance with data governance, privacy, and ethical AI standards.
• Product & Partner Evaluation
• Identify new tools, products, and partners to accelerate platform development.
• Make informed build vs. buy decisions for data and AI components.
• Innovation
• Introduce advanced techniques like synthetic data generation, automated annotation, and AI-driven data quality checks.
  

• Enterprise Architecture: Proven experience in designing large-scale, cloud-native platforms.
• Multi-Cloud Expertise: Hands-on experience with AWS, Azure, and GCP services and products.
• Data Engineering: Strong background in data pipelines, ETL, big data technologies (Spark, Hadoop).
• AI/ML Knowledge: Deep understanding of AI/ML lifecycle and data preparation for model training.
• Platform Engineering: Expertise in containerization (Docker/Kubernetes), microservices, and API-driven design.
• DevOps & Automation: Experience with CI/CD pipelines, Infrastructure as Code (Terraform, CloudFormation).
• Strategic Product Evaluation: Ability to assess third-party tools and make build vs. buy decisions.
• Leadership: Ability to guide technical teams and ensure architectural integrity.
  
  

• Experience with AI data platforms, MLOps, and data labeling tools.
• TOGAF or similar enterprise architecture certification.
• Advanced degree in Computer Science, Data Science, or related field.