Robotsandpencils

VP, Solutions Architect - AWS

engineeringfull-timeUS - Remote

SALARY

Not listed

WORK TYPE

remote

JOB TYPE

full-time

INDUSTRY

general

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About the role

Robots &amp; 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.&nbsp; &nbsp; As an AWS AI Solutions Architect, you will serve as a strategic technical advisor—translating ambiguity into structured AWS-native architectures,&nbsp;validating&nbsp;designs through hands-on prototyping, and ensuring every solution aligns with the AWS Well-Architected Framework (including ML Lens) while delivering measurable business value.&nbsp;

Key Responsibilities&nbsp;

Client Engagement &amp; AWS Solutions&nbsp;Architecture&nbsp;

<ul>

<li>Serve as the primary AWS AI architecture partner for strategic clients, driving generative and agentic AI system design from discovery through production.&nbsp;</li>

<li>Lead architecture design using Amazon Bedrock (including foundation models and custom models), Bedrock&nbsp;AgentCore, AWS Strands Agents, and AWS&nbsp;AgentCore&nbsp;Gateway.&nbsp;</li>

<li>Design advanced RAG, Agentic RAG, and multi-agent orchestration architectures&nbsp;leveraging&nbsp;AWS-native services such as Lambda, Step Functions, API Gateway, DynamoDB, Aurora (pgvector), and OpenSearch.&nbsp;</li>

<li>Produce AWS reference architectures, architecture decision records (ADRs), and&nbsp;implementation&nbsp;roadmaps aligned to business&nbsp;objectives.&nbsp;</li>

<li>Validate&nbsp;feasibility through hands-on prototyping in Python using Bedrock SDKs, SageMaker, and serverless services.&nbsp;</li>

<li>Ensure architectures follow AWS security best practices (IAM, KMS, VPC,&nbsp;PrivateLink)&nbsp;and cost optimization principles.&nbsp;</li>

</ul>

Outcome Ownership &amp; Business Impact&nbsp;

<ul>

<li>Own architectural integrity from concept through production deployment on AWS.&nbsp;</li>

<li>Align solutions with AWS Well-Architected Framework pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.&nbsp;</li>

<li>Guide clients through tradeoff decisions across model selection (Bedrock FMs vs custom SageMaker models), latency, cost, governance, and compliance.&nbsp; Accelerate time-to-value through reusable AWS accelerators, Infrastructure as Code (CloudFormation/Terraform/CDK), and CI/CD automation.&nbsp;</li>

<li>Continuously evaluate emerging AWS AI capabilities (Nova Forge, Nova 2 Sonic, Bedrock updates, and new&nbsp;AgentCore&nbsp;capabilities).&nbsp;</li>

</ul>

Engineering Leadership &amp; Delivery Excellence&nbsp;

<ul>

<li>Provide architectural oversight to Forward Deployed Engineers and AWS delivery teams.&nbsp;</li>

<li>Establish&nbsp;best practices for&nbsp;MLOps&nbsp;on AWS including model lifecycle management, monitoring, and observability using SageMaker, CloudWatch, CloudTrail, and AWS Config.&nbsp;</li>

<li>Define governance, responsible AI guardrails, Bedrock Guardrails configuration, and security controls for enterprise environments.&nbsp;</li>

<li>Mentor engineers on AWS AI service integration, distributed systems design, and secure multi-account strategies.&nbsp;</li>

<li>Make principled tradeoffs under constraints related to privacy, compliance (SOC2, HIPAA, GDPR), cost, and operational complexity.&nbsp;</li>

</ul>

Cross-Functional Collaboration&nbsp;

<ul>

<li>Partner with internal product, engineering, research, and customer success teams to evolve AWS-based AI offerings.&nbsp;</li>

<li>Contribute AWS reference architectures and reusable infrastructure modules to internal accelerators.&nbsp;</li>

<li>Support pre-sales engagements including architecture workshops, AWS migration strategy, and solution scoping.&nbsp;</li>

<li>Collaborate across distributed teams and client stakeholders across North America.&nbsp;</li>

</ul>

Required Skills &amp; Qualifications&nbsp;

<ul>

<li>Bachelor’s degree in Computer Science, Engineering, or equivalent experience.</li>

<li>10+ years of experience in software engineering or cloud architecture with deep AWS ownership.&nbsp;</li>

<li>Deep expertise in Amazon Bedrock, Bedrock AgentCore, AWS Strands Agents, AgentCore Gateway, and related AWS AI services.&nbsp;</li>

<li>Strong familiarity with SageMaker (training, deployment, pipelines), deep learning fundamentals, and model fine-tuning strategies.&nbsp;</li>

<li>Experience architecting RAG, multi-agent, and orchestration systems using AWS-native services.&nbsp;</li>

<li>Strong knowledge of distributed systems, event-driven architectures, and serverless patterns.&nbsp;</li>

<li>Proficiency with Infrastructure as Code (AWS CDK, CloudFormation, Terraform).&nbsp;</li>

<li>Hands-on development capability in Python and AWS SDKs.&nbsp;</li>

<li>Experience implementing observability and monitoring strategies in AWS environments.&nbsp;</li>

<li>Proven success leading enterprise-scale AWS transformations.</li>

<li>Exceptional communication skills for both technical and executive audiences.&nbsp;</li>

<li>AWS Professional Certifications highly preferred (AWS Solutions Architect – Professional, AWS DevOps Engineer – Professional).&nbsp;</li>

</ul>

Nice to Have&nbsp;

<ul>

<li> AWS Specialty certifications (Machine Learning – Specialty, Security – Specialty).</li>

<li>Experience with advanced agentic reasoning patterns (ReAct,CoT, Tree-of-Thoughts) implemented on Bedrock.&nbsp;</li>

<li>Experience building secure multi-account AWS organizations using Control Tower.&nbsp;</li>

<li>Exposure to data engineering services such as Glue, Redshift, Lake Formation.&nbsp;</li>

<li>Consulting or professional services background.&nbsp;</li>

</ul>

Personal Competencies&nbsp;

<ul>

<li> Accountability – Owns AWS architectural direction and client outcomes with rigor.</li>

<li>Adaptability – Rapidly adopts new AWS AI releases and evolving generative AI capabilities.</li>

<li>Collaboration – Builds trust across engineering and executive stakeholders.&nbsp;</li>

<li>Execution-Focused – Balances innovation with production-ready AWS delivery.&nbsp;</li>

<li>Innovation-Minded – Experiments responsibly with emerging AWS AI services.&nbsp;</li>

<li>Craftsmanship – Designs secure, scalable, and well-documented AWS systems.&nbsp;</li>

<li>Leadership with Courage – Drives architectural alignment in complex environments.&nbsp;</li>

<li>Comfort in Ambiguity – Translates unclear AI requirements into AWS-native solution architectures.&nbsp;</li>

</ul>

&nbsp;

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