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Robotsandpencilsvia Greenhouse

VP of Solutions Architect - AI

REMOTEPosted 5d ago
OtherExecutiveFull-time

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About the Role

Robots & Pencils is seeking a seasoned AWS AI Solutions Architect to lead the design and delivery of complex, enterprise-grade generative and agentic AI systems built on Amazon Web Services. You will architect scalable, secure, and production-ready AI platforms leveraging Amazon Bedrock, Amazon Bedrock AgentCore, AWS Strands Agents, AWS AgentCore Gateway, Nova Forge, Nova 2 Sonic, and related AWS AI/ML services. 
 
As an AWS AI Solutions Architect, you will serve as a strategic technical advisor—translating ambiguity into structured AWS-native architectures, validating designs through hands-on prototyping, and ensuring every solution aligns with the AWS Well-Architected Framework (including ML Lens) while delivering measurable business value. 

Key Responsibilities 

Client Engagement & AWS Solutions Architecture 

  • Serve as the primary AWS AI architecture partner for strategic clients, driving generative and agentic AI system design from discovery through production. 
  • Lead architecture design using Amazon Bedrock (including foundation models and custom models), Bedrock AgentCore, AWS Strands Agents, and AWS AgentCore Gateway. 
  • Design advanced RAG, Agentic RAG, and multi-agent orchestration architectures leveraging AWS-native services such as Lambda, Step Functions, API Gateway, DynamoDB, Aurora (pgvector), and OpenSearch. 
  • Produce AWS reference architectures, architecture decision records (ADRs), and implementation roadmaps aligned to business objectives. 
  • Validate feasibility through hands-on prototyping in Python using Bedrock SDKs, SageMaker, and serverless services. 
  • Ensure architectures follow AWS security best practices (IAM, KMS, VPC, PrivateLink) and cost optimization principles. 

Outcome Ownership & Business Impact 

  • Own architectural integrity from concept through production deployment on AWS. 
  • Align solutions with AWS Well-Architected Framework pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability. 
  • Guide clients through tradeoff decisions across model selection (Bedrock FMs vs custom SageMaker models), latency, cost, governance, and compliance. 
    Accelerate time-to-value through reusable AWS accelerators, Infrastructure as Code (CloudFormation/Terraform/CDK), and CI/CD automation. 
  • Continuously evaluate emerging AWS AI capabilities (Nova Forge, Nova 2 Sonic, Bedrock updates, and new AgentCore capabilities). 

Engineering Leadership & Delivery Excellence 

  • Provide architectural oversight to Forward Deployed Engineers and AWS delivery teams. 
  • Establish best practices for MLOps on AWS including model lifecycle management, monitoring, and observability using SageMaker, CloudWatch, CloudTrail, and AWS Config. 
  • Define governance, responsible AI guardrails, Bedrock Guardrails configuration, and security controls for enterprise environments. 
  • Mentor engineers on AWS AI service integration, distributed systems design, and secure multi-account strategies. 
  • Make principled tradeoffs under constraints related to privacy, compliance (SOC2, HIPAA, GDPR), cost, and operational complexity. 

Cross-Functional Collaboration 

  • Partner with internal product, engineering, research, and customer success teams to evolve AWS-based AI offerings. 
  • Contribute AWS reference architectures and reusable infrastructure modules to internal accelerators. 
  • Support pre-sales engagements including architecture workshops, AWS migration strategy, and solution scoping. 
  • Collaborate across distributed teams and client stakeholders across North America. 

Required Skills & Qualifications 

  • Bachelor’s degree in Computer Science, Engineering, or equivalent experience.
  • 10+ years of experience in software engineering or cloud architecture with deep AWS ownership. 
  • Deep expertise in Amazon Bedrock, Bedrock AgentCore, AWS Strands Agents, AgentCore Gateway, and related AWS AI services. 
  • Strong familiarity with SageMaker (training, deployment, pipelines), deep learning fundamentals, and model fine-tuning strategies. 
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