Chennai, Tamil Nadu
Job Summary
AWS AI Architect (Implementation Lead)
Role Summary

Lead implementation and delivery of an AWS-based Quality Documents (QMS) solution using an approved reference architecture. Focus on translating architecture into production-ready capabilities using Amazon Bedrock, AgentCore, Textract, Comprehend, A2I, Lambda, Glue, and related AWS services.

Key Responsibilities
Lead implementation of the approved IDP reference architecture on AWS.
Configure and operationalize Amazon Bedrock and AgentCore workflows for document classification and processing.
Provide technical leadership and implementation guidance to engineering teams.
Define integration patterns and deployment standards aligned to the reference architecture.
Oversee document ingestion, OCR, extraction, classification, validation, and human review workflows.
Guide integration with enterprise applications and downstream systems.
Review code, deployment artifacts, and performance optimization recommendations.
Ensure adherence to AWS security, governance, and compliance standards.
Establish monitoring, observability, and operational support frameworks.
Support production deployment, hypercare, and knowledge transfer.
Collaborate with stakeholders to ensure successful delivery outcomes.
Drive technical risk identification and mitigation.
Required Skills
Hands-on experience with Amazon Bedrock, Bedrock Agents, and AgentCore.
Experience delivering Document AI and Intelligent Document Processing solutions.
Expertise with Amazon Textract, Amazon Comprehend, Amazon A2I, AWS Glue,



Lambda, and S3.
Strong understanding of enterprise integration and event-driven architectures.
Experience leading implementation teams and design reviews.
Knowledge of AI governance, monitoring, and production support.
Working knowledge of Python and cloud-native development.
Familiarity with CI/CD and DevOps practices.
Key Responsibilities
1. Architect And Design Enterprise Data Pipelines And Etl Workflows Using Amazon Glue, Ensuring Scalable, Secure, And Effective Data Integration Across Multiple Sources.
2. Define And Implement Data Warehousing Solutions Leveraging Amazon Redshift And Snowflake, Optimizing For Performance, Cost, And Reliability.
3. Lead The Adoption Of Devops Practices For Data Engineering By Automating Deployment, Monitoring, And Ci/Cd Pipelines For Aws-Based Data Platforms.
4. Provide Architectural Governance By Reviewing And Validating Solution Designs, Ensuring Compliance With Organizational And Industry Standards.
5. Guide The Team In Developing Advanced Data Transformation Scripts Using Python, Enabling Complex Data Processing And Analytics Use Cases.
6. Drive Technology Innovation By Evaluating Emerging Aws Services And Data Engineering Tools,



Recommending Adoption To Enhance Solution Capabilities.
7. Mentor And Upskill Team Members In Amazon Glue, Redshift, Snowflake, And Python, Fostering A Culture Of Continuous Learning And Technical Excellence.
8. Represent The Organization By Submitting Whitepapers, Participating In Industry Forums, And Contributing To Patent Filings In The Data Architecture Domain.
Skill Requirements
1. Architectural Leadership In Amazon Glue For Designing And Orchestrating Complex Etl Solutions.
2. Advanced Proficiency In Amazon Redshift And Snowflake For Data Warehousing And Analytics.
3. ExpertLevel Skills In Python For Data Engineering And Automation Tasks.
4. InDepth Experience With Devops Practices And Tools (E.G., Aws Codepipeline, Cloudformation, Terraform) For Data Platform Automation.
5. Solid Understanding Of Data Governance, Security, And Compliance Within Aws Environments.
6. Excellent Ability To Translate Business Requirements Into Scalable, CloudNative Data Architectures.

Other Requirements
1. Aws Certified Solutions Architect � Professional (Recommended)
2. Aws Certified Data Analytics � Specialty (Optional But Valuable)
3. Snowflake Snowpro Advanced Architect Certification (Optional But Valuable
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