Solutions Architect
AWS is hiring for Solutions Architects, who embeds directly inside India's most strategic enterprise accounts to architect production AI systems that deliver measurable business impact. The role will support complete engagement from discovery and scoping, through architecture and help build/co-build.
Key job responsibilities
1. End-to-End Agentic AI Deployment
• Lead discovery workshops with customer CTO / engineering teams to map high-value AI use cases across multiple industry verticals.
• Own technical scoping, solution architecture for agentic AI workloads on Amazon Bedrock and AgentCore.
• Build PoCs leveraging Amazon Bedrock, Strands Agents SDK, Amazon AgentCore
• Design and implement multi-agent orchestrations, MCP tool servers, RAG pipelines (Knowledge Bases for Bedrock, OpenSearch Serverless, Aurora pgvector), and LLM-as-judge evaluation frameworks.
• Deliver IaC (AWS CDK / CloudFormation / Terraform) for repeatable, production-grade deployment patterns that the customer can own post-engagement.
2. Regulated Industry Delivery
• Navigate India's regulatory and translate requirements into Bedrock Guardrails, Automated Reasoning Checks, and data residency configurations.
• Architect AI systems for auditability: model version pinning, prompt logging, PII redaction, inference geography (ap-south-1), and compliance artifact generation.
• Deliver eval-driven acceptance criteria: define benchmark suites, run eval loops against domain-specific test sets, and produce launch-evidence packages that satisfy customer risk / model governance teams.
3. Customer Engineering Partnership
• Run structured discovery (jobs-to-be-done, process mining, failure mode analysis) to identify AI leverage points and sequence delivery for maximum early impact.
• Build long-term technical relationships with customer engineering leadership and proactively surface new AI deployment opportunities across the account lifecycle.
4. AWS Field Signal & Product Influence
• Codify repeatable deployment patterns into AWS-wide playbooks, reference architectures, and GitHub samples that scale insights across hundreds of customers.
• Feed structured field signal (model gaps, tooling friction, feature requests, eval results) to AWS Engineering teams
• Collaborate with AWS SA leadership, Specialists, Partner SA, and ISV teams to deliver joint engagements and avoid duplicating effort.
• Represent AWS AI at customer EBCs, industry conferences, and CXO briefings — you are the technical face of AWS's GenAI capability in the field.
Key job responsibilities
1. End-to-End Agentic AI Deployment
• Lead discovery workshops with customer CTO / engineering teams to map high-value AI use cases across multiple industry verticals.
• Own technical scoping, solution architecture for agentic AI workloads on Amazon Bedrock and AgentCore.
• Build PoCs leveraging Amazon Bedrock, Strands Agents SDK, Amazon AgentCore
• Design and implement multi-agent orchestrations, MCP tool servers, RAG pipelines (Knowledge Bases for Bedrock, OpenSearch Serverless, Aurora pgvector), and LLM-as-judge evaluation frameworks.
• Deliver IaC (AWS CDK / CloudFormation / Terraform) for repeatable, production-grade deployment patterns that the customer can own post-engagement.
2. Regulated Industry Delivery
• Navigate India's regulatory and translate requirements into Bedrock Guardrails, Automated Reasoning Checks, and data residency configurations.
• Architect AI systems for auditability: model version pinning, prompt logging, PII redaction, inference geography (ap-south-1), and compliance artifact generation.
• Deliver eval-driven acceptance criteria: define benchmark suites, run eval loops against domain-specific test sets, and produce launch-evidence packages that satisfy customer risk / model governance teams.
3. Customer Engineering Partnership
• Run structured discovery (jobs-to-be-done, process mining, failure mode analysis) to identify AI leverage points and sequence delivery for maximum early impact.
• Build long-term technical relationships with customer engineering leadership and proactively surface new AI deployment opportunities across the account lifecycle.
4. AWS Field Signal & Product Influence
• Codify repeatable deployment patterns into AWS-wide playbooks, reference architectures, and GitHub samples that scale insights across hundreds of customers.
• Feed structured field signal (model gaps, tooling friction, feature requests, eval results) to AWS Engineering teams
• Collaborate with AWS SA leadership, Specialists, Partner SA, and ISV teams to deliver joint engagements and avoid duplicating effort.
• Represent AWS AI at customer EBCs, industry conferences, and CXO briefings — you are the technical face of AWS's GenAI capability in the field.