AWS GenAI Architect
AWS GenAI Architect
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Job Description
AWS GenAI Architect Onshore Boston
Job Summary:
We are seeking a highly skilled and innovative GenAI Architect to lead the design implementation and governance of next-generation AI solutions leveraging AWS-native services. The ideal candidate will have deep AWS GenAI services security frameworks service enablement patterns and infrastructure automation using Terraform. This role will be instrumental in architecting scalable secure and responsible AI systems for enterprise-grade applications.
Roles & Responsibilities:
Architecture & Design
Architect and design Generative AI solutions using Amazon Bedrock SageMaker Amazon Q and Agents for Amazon Bedrock.
Define multi-tenant and scalable architectures that ensure data security isolation and performance.
Develop architecture blueprints and high-level design documents for GenAI workloads.
Security & Governance
Define and implement security guardrails across the GenAI ecosystem (IAM SCPs encryption network boundaries).
Ensure responsible AI practices including data governance privacy bias mitigation and human-in-the-loop mechanisms.
Establish policies for prompt management model access control and API usage monitoring.
Service Enablement & Integration
Enable and onboard AI capabilities as reusable services and APIs for enterprise-wide adoption.
Integrate GenAI services with backend systems enterprise data lakes DynamoDB and external APIs.
Orchestrate AI workflows using Amazon Q and agents for complex enterprise tasks and reasoning.
Automation & Infrastructure
Implement and manage GenAI infrastructure using Terraform with emphasis on reusable modules compliance checks and CI/CD.
Collaborate with Platform/DevOps teams to build standardized environments for rapid experimentation and deployment.
Innovation & Evangelism
Act as a thought leader on GenAI sharing emerging trends and best practices across teams and leadership.
Conduct POCs and technical workshops to drive AI adoption and maturity within the organization.
Contribute to internal knowledge repositories and reusable solution accelerators.
Preferred Qualifications:
AWS Certified Machine Learning Specialty or AWS Solutions Architect certifications.
Experience working in regulated industries (e.g. finance healthcare).
Exposure to Open Source LLMs (LLaMA Mistral Falcon) and foundation model fine-tuning practices.
Familiarity with MLOps pipelines including model lifecycle management and versioning.
Requirements
12 to 15 years in solution architecture or AI/ML engineering with 4 years in GenAI-related projects.
Strong expertise in Amazon Bedrock SageMaker (incl. JumpStart Pipelines Model Registry) Amazon Q and Agents for Bedrock.
Deep understanding of AWS IAM VPC KMS and security best practices for GenAI workloads.
Hands-on experience with DynamoDB including data modeling performance tuning and integration.
Solid skills in Terraform (preferably Terraform Enterprise) for infrastructure provisioning and automation.
Understanding of GenAI concepts including RAG (Retrieval Augmented Generation) prompt engineering vector databases model evaluation and fine-tuning.
Proficient in Python and experience with AWS SDKs LangChain or similar frameworks is a plus.
Excellent communication and stakeholder management skills across business and engineering teams.
Hands-on experience with DynamoDB including data modeling performance tuning and integration.
12 to 15+ years in solution architecture or AI/ML engineering, with 4+ years in GenAI-related projects. Strong expertise in Amazon Bedrock, SageMaker (incl. JumpStart, Pipelines, Model Registry), Amazon Q, and Agents for Bedrock. Deep understanding of AWS IAM, VPC, KMS, and security best practices for GenAI workloads. Hands-on experience with DynamoDB, including data modeling, performance tuning, and integration. Solid skills in Terraform (preferably Terraform Enterprise) for infrastructure provisioning and automation. Understanding of GenAI concepts including RAG (Retrieval Augmented Generation), prompt engineering, vector databases, model evaluation, and fine-tuning.
Education
12 to 15+ years in solution architecture or AI/ML engineering, with 4+ years in GenAI-related projects. Strong expertise in Amazon Bedrock, SageMaker (incl. JumpStart, Pipelines, Model Registry), Amazon Q, and Agents for Bedrock. Deep understanding of AWS IAM, VPC, KMS, and security best practices for GenAI workloads. Hands-on experience with DynamoDB, including data modeling, performance tuning, and integration. Solid skills in Terraform (preferably Terraform Enterprise) for infrastruct
Employment Type
Full Time
