Principal Solutions Architect

About This Role

This Principal Solutions Architect role combines deep technical expertise with the ability to build scalable mechanisms that amplify the effectiveness of the Startup Solutions Architecture team. It requires a blend of builder’s depth and strategic impact, enabling someone to design, code, and operate systems at scale while creating programs, frameworks, and reusable assets that multiply impact across hundreds of startups. The successful candidate will shape how AWS engages startups at scale, working at the frontier of cloud, data, and AI innovation.

Responsibilities

• Design and implement scalable programs, reusable assets, and automation tools that amplify the impact of the Startup SA organization
• Create reference architectures, IaC templates, and enablement content to accelerate solution delivery across startups
• Develop systematic approaches to capture, codify, and share technical insights from the field back into the organization
• Mentor SAs and drive technical upskilling programs that elevate the entire team’s capabilities
• Architect training and inference systems at scale (distributed training, LoRA/QLoRA, quantization, GPU/TPU optimization)
• Design end-to-end ML pipelines and MLOps workflows (data prep, labeling, model registry, deployment)
• Build and optimize model serving architectures and inference systems for cost and latency
• Lead design and operations excellence for Kubernetes/EKS, service mesh, microservices, and serverless-first architectures
• Ensure security, compliance, and observability in containerized workloads
• Drive GitOps-first workflows, CI/CD at scale, IaC (Terraform/CDK), and automated testing frameworks
• Apply SRE best practices: monitoring, incident management, resilience, and scaling patterns
• Advise startups on proprietary data strategy as a moat: collection, quality, labeling, governance
• Architect data pipelines (ETL/ELT, streaming, orchestration) and integrate with modern analytics platforms (Snowflake, Redshift, BigQuery, Databricks)
• Guide adoption of feature stores, vector databases, and lakehouse patterns to enable ML/RAG use cases
• Ensure compliance and governance across sensitive workloads (GDPR, HIPAA, SOC2)
• Maintain deep understanding of emerging technical trends in GenAI, cloud, and data ecosystems
• Translate insights into actionable guidance for startups and scalable mechanisms for the organization
• Provide forward-looking perspectives on technology adoption, evolution, and competitive advantage
• Build repeatable solutions and reference architectures addressing common startup pain points
• Publish blogs, whitepapers, open-source repos, and technical presentations that extend reach beyond direct engagements