Robots and Pencils - VP of Solutions Architect - AI
Requirements
• 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. • Experience architecting RAG, multi-agent, and orchestration systems using AWS-native services. • Strong knowledge of distributed systems, event-driven architectures, and serverless patterns. • Proficiency with Infrastructure as Code (AWS CDK, CloudFormation, Terraform). • Hands-on development capability in Python and AWS SDKs. • Experience implementing observability and monitoring strategies in AWS environments. • Proven success leading enterprise-scale AWS transformations. • Exceptional communication skills for both technical and executive audiences. • AWS Professional Certifications highly preferred (AWS Solutions Architect – Professional, AWS DevOps Engineer – Professional). • AWS Specialty certifications (Machine Learning – Specialty, Security – Specialty). • Experience with advanced agentic reasoning patterns (ReAct,CoT, Tree-of-Thoughts) implemented on Bedrock. • Experience building secure multi-account AWS organizations using Control Tower. • Exposure to data engineering services such as Glue, Redshift, Lake Formation. • Consulting or professional services background. • Personal Competencies • Accountability – Owns AWS architectural direction and client outcomes with rigor. • Adaptability – Rapidly adopts new AWS AI releases and evolving generative AI capabilities. • Collaboration – Builds trust across engineering and executive stakeholders. • Execution-Focused – Balances innovation with production-ready AWS delivery. • Innovation-Minded – Experiments responsibly with emerging AWS AI services. • Craftsmanship – Designs secure, scalable, and well-documented AWS systems. • Leadership with Courage – Drives architectural alignment in complex environments. • Comfort in Ambiguity – Translates unclear AI requirements into AWS-native solution architectures.
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.
Benefits
• We build smart systems for a human world — blending creativity, engineering, and AWS-powered AI to help organizations reimagine how they work. As an AWS AI Solutions Architect, you will shape enterprise-scale generative and agentic AI platforms using the most advanced AWS services available. You will define architectures that deliver measurable business value, mentor teams, and directly influence the evolution of our AWS AI practice.
Apply in one click
Upload My Resume
Drop here or click to browse · Tap to choose · PDF, DOCX, DOC, RTF, TXT