Sr Solution Architect

Job Summary

Experience: 17+ years of overall IT experience, including at least 3 years in AI/ML and production Generative AI architecture using Amazon Bedrock, Bedrock Knowledge Bases, Bedrock Guardrails, and Bedrock AgentCore Runtime, Gateway, Memory, Identity, Evaluations, and Observability.

Job Summary: We are looking for an experienced Generative AI Architect - AWS Generative AI and Agentic AI to design, build, and deliver secure, scalable, production-grade AI solutions using Amazon Bedrock, Bedrock Knowledge Bases, Bedrock Guardrails, and Bedrock AgentCore Runtime, Gateway, Memory, Identity, Evaluations, and Observability. The candidate will translate business requirements into measurable technical outcomes and apply strong engineering, observability, security, governance, and Responsible AI practices.

Key Responsibilities

Key Responsibilities:

• Architect and govern Generative AI and agentic AI solutions using Amazon Bedrock, Bedrock Knowledge Bases, Bedrock Guardrails, and Bedrock AgentCore Runtime, Gateway, Memory, Identity, Evaluations, and Observability.
• Define enterprise standards for end-to-end implementation from requirements and data preparation through evaluation, deployment, and production support.
• Translate business requirements into solution designs, technical plans, delivery estimates, and measurable acceptance criteria.
• Build secure integrations with enterprise applications, APIs, data platforms, and operational workflows.
• Establish evaluation, monitoring, tracing, cost, latency, safety, and reliability controls for production solutions.
• Provide technical direction for reusable components, development standards, documentation, testing, CI/CD, and release governance.
• Lead architecture reviews, stakeholder workshops, framework selection, governance decisions, and technical mentoring across teams.

Skill Requirements

Must Have Skills:

• Extensive architecture and delivery experience with Amazon Bedrock and Models: Amazon Bedrock, Converse API, Amazon Nova, Anthropic Claude, cross-Region inference profiles.
• Extensive architecture and delivery experience with Bedrock AgentCore: Amazon Bedrock AgentCore Runtime, Harness, Memory, Gateway, Identity.
• Extensive architecture and delivery experience with RAG and Knowledge Bases: Amazon Bedrock Knowledge Bases, Amazon S3, Amazon OpenSearch Serverless, Amazon Aurora, Amazon Neptune Analytics.
• Extensive architecture and delivery experience with Agents and Orchestration: Agents for Amazon Bedrock, AWS Lambda, LangGraph, LangChain, Strands Agents.
• Extensive architecture and delivery experience with Prompt Engineering and Evaluation: Amazon Bedrock prompt management, Bedrock evaluations, Bedrock Guardrails.
• Extensive architecture and delivery experience with Security and Responsible AI: AWS IAM, AWS KMS, AWS Secrets Manager, Amazon Cognito, Amazon VPC.

  Preferred Skills:

  • Working knowledge of Deployment, LLMOps and Observability, including Amazon CloudWatch, AWS X-Ray, AWS CloudTrail, Amazon ECR.
  • Working knowledge of Data and Multimodal AI, including Amazon S3, AWS Glue, AWS Lake Formation, Amazon Athena.

Other Requirements

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related field.
• 13+ years of overall IT experience, including at least 3 years in AI/ML and production Generative AI architecture.
• Strong understanding of LLMs, RAG, agentic automation, prompt engineering, evaluation, and production AI operations.
• Ability to design secure and reliable APIs, tool integrations, data flows, identity controls, and failure-handling mechanisms.
• Experience implementing automated testing, version control, deployment pipelines, monitoring, and rollback practices.
• Good understanding of Responsible AI, privacy, safety, compliance, and enterprise security standards.
• Proven experience leading complex AI/ML initiatives and resolving cross-team technical dependencies.
• Demonstrated ability to mentor engineers, review designs, and improve engineering quality and delivery predictability.
• Demonstrated enterprise architecture experience covering scalability, resilience, security, governance, data boundaries, and platform operating models.
• Ability to evaluate platforms and frameworks using measurable criteria and communicate architectural trade-offs to senior stakeholders.