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Aws Architect

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Onsite

Aws Architect

HCLTech

Aws Architect

Skills

PythonAWS LambdaAIAWSCompliance

2 months ago

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Important information


Contract type:

Permanent

Salary:

₹1,000,000 – ₹1,800,000 / year

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

1 September 2026

What they need


Mail your resume to [email protected]

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

  1. Lead implementation of the approved IDP reference architecture on AWS.

  2. Configure and operationalize Amazon Bedrock and AgentCore workflows for document classification and processing.

  3. Provide technical leadership and implementation guidance to engineering teams.

  4. Define integration patterns and deployment standards aligned to the reference architecture.

  5. Oversee document ingestion, OCR, extraction, classification, validation, and human review workflows.

  6. Guide integration with enterprise applications and downstream systems.

  7. Review code, deployment artifacts, and performance optimization recommendations.

  8. Ensure adherence to AWS security, governance, and compliance standards.

  9. Establish monitoring, observability, and operational support frameworks.

  10. Support production deployment, hypercare, and knowledge transfer.

  11. Collaborate with stakeholders to ensure successful delivery outcomes.

  12. Drive technical risk identification and mitigation.

Required Skills

  1. Hands-on experience with Amazon Bedrock, Bedrock Agents, and AgentCore.

  2. Experience delivering Document AI and Intelligent Document Processing solutions.

  3. Expertise with Amazon Textract, Amazon Comprehend, Amazon A2I, AWS Glue, Lambda, and S3.

  4. Strong understanding of enterprise integration and event-driven architectures.

  5. Experience leading implementation teams and design reviews.

  6. Knowledge of AI governance, monitoring, and production support.

  7. Working knowledge of Python and cloud-native development.

  8. Familiarity with CI/CD and DevOps practices.

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