Solution Architect

What matters most

• Hands-on Technical Prototyping

  The role requires building functional proofs of concept and demos using Python or TypeScript. You must be prepared to demonstrate proficiency in writing clean, deployable code and using infrastructure-as-code tools like CDK or Terraform to create repeatable environments for AWS-based solutions.

• Generative AI Solutioning

  You must translate business requirements into technical architectures involving LLMs, RAG, and AI agents. Prioritize deep familiarity with Amazon Bedrock and the ability to explain how these AI components integrate with core AWS services like DynamoDB, S3, and OpenSearch to solve specific customer problems.

• Cross-Functional Communication

  Success depends on bridging the gap between technical teams and business stakeholders. You must be able to adapt complex architectural concepts for diverse audiences, ranging from deep-dive technical discussions with engineers to high-level value propositions for CXOs.

• Pre-sales Lifecycle Management

  You are responsible for the end-to-end technical pre-sales journey, including discovery, RFP/RFI responses, and technical workshops. You must demonstrate the ability to manage multiple concurrent opportunities while ensuring a seamless transition of requirements to delivery teams.

Likely challenges to think through

• Balancing Speed and Technical Depth

  In a pre-sales environment, you must build demos quickly while maintaining architectural integrity. Prepare by practicing how to scope a POC to its essential features to meet tight deadlines without compromising on security or performance standards.

• Handling Technical Objections

  Customers will challenge your proposed architectures on grounds of cost, security, and compliance. Prepare by developing a framework to address these concerns proactively during presentations, ensuring you can defend your design choices against real-world constraints.

• Context Switching Across Opportunities

  Managing multiple customer engagements simultaneously requires high organizational discipline. Prepare by demonstrating your methodology for tracking technical requirements, documentation, and follow-up actions to ensure no detail is lost during the transition between different client projects.

Topics to prepare

• Generative AI Architecture Patterns

  Focus on the RAG (Retrieval-Augmented Generation) lifecycle and the design of AI agents. Exercise: Draft a high-level architecture diagram showing how Amazon Bedrock, OpenSearch, and S3 integrate to provide context-aware responses for a hypothetical enterprise customer. This is relevant to the role's requirement for building POCs for GenAI and Agentic AI solutions.

• Pre-sales Technical Discovery and RFP Strategy

  Understand how to translate high-level business pain points into specific AWS service mappings. Exercise: Review a sample RFP requirement for a cloud migration or AI implementation and practice creating a structured response that includes an architecture overview, effort estimation, and risk mitigation. This directly supports the responsibility of managing the pre-sales journey and responding to RFPs.

• Cloud-Native Infrastructure as Code (IaC)

  Master the principles of repeatable environment deployment. Exercise: Create a simple AWS CDK or Terraform script that deploys an S3 bucket and a Lambda function. This is essential for the role's requirement to build repeatable demo environments and manage infrastructure as code.

Skills to revise

• Hands-on Prototyping with AWS Bedrock

  Gain proficiency in using the AWS Bedrock API to invoke foundation models. Exercise: Write a Python script that uses the Bedrock runtime to perform a basic RAG task, such as querying a document stored in S3. This is a core requirement for building the POCs mentioned in the job description.

• Technical Presentation for Diverse Audiences

  Practice adapting technical content for both developers and CXOs. Exercise: Prepare a 5-minute pitch for a GenAI solution; record yourself explaining the technical architecture to an engineer, then re-explain the value proposition and ROI to a CXO. This is critical for the role's requirement to present to varied stakeholders.

• Prompt Engineering and Optimization

  Develop techniques for improving LLM output quality. Exercise: Take a complex business query and iterate on a system prompt to improve accuracy and reduce hallucinations using a playground environment. This is directly relevant to the stated need for expertise in prompt engineering and LLM application development.

• Develop a Generative AI Proof of Concept

  Create a functional RAG-based application using Amazon Bedrock, Python or TypeScript, and OpenSearch. Prepare a brief walkthrough that demonstrates how you handle prompt engineering and agentic workflows to solve a specific business problem.

• Prepare an Architecture Presentation for Diverse Stakeholders

  Draft a slide deck or whiteboard diagram that explains a complex AWS cloud-native solution. Ensure you can articulate the technical implementation for engineers while simultaneously highlighting the business value, cost-efficiency, and security benefits for a CXO audience.

• Curate a Portfolio of Infrastructure as Code Samples

  Assemble a repository of your previous work using Terraform or AWS CDK. Focus on examples that show how you automate the deployment of repeatable demo environments, as this is critical for the pre-sales velocity required in this role.

• Formulate Responses to Technical Discovery Scenarios

  Prepare structured answers for common pre-sales challenges, such as handling technical objections regarding data privacy in GenAI, managing scope creep during a POC, or explaining the transition process from pre-sales to delivery teams.

• Draft Strategic Questions for the Interviewers

  Prepare insightful questions regarding the team's current pre-sales asset library, the typical sales cycle length for their GenAI offerings, and how they currently collaborate with AWS partner teams on co-selling activities.