Solutions Architect

Job Summary

The Solutions Architect reviews, designs, and refactors distributed systems blueprints to train and evaluate AI infrastructure tools. Core daily duties include auditing AI-generated microservices configurations and deployment scripts, verifying Kubernetes container orchestration rules, identifying complex system bottlenecks or cloud security hazards, and writing structured, institutional-grade technical breakdowns to train frontier LLMs in scalable deployment practices.

Foundational Engineering & Infrastructure Prerequisites

• Microservices Specialization: Proven expertise architecting, deploying, and optimizing scalable, production-grade microservices-based applications.
• Cloud Ecosystem Depth: Deep hands-on experience with Amazon Web Services (AWS) core components, including compute, networking, security, storage, and infrastructure-as-code paradigms.
• Containerization Mastery: Total mastery of container runtimes using Docker, paired with expert-level cloud-native container orchestration using Kubernetes.
• Analytical Problem Solving: Strong analytical skills and abstract thinking, capable of breaking down complex enterprise requirements into secure, high-performance distributed blueprints.
• Communication Articulation: Exceptional written and verbal English communication skills, with a critical emphasis on explaining complex architectural decisions, trade-offs, and errors clearly and succinctly.
• Distributed Experience: Proven ability to operate successfully, efficiently, and independently within a distributed, remote-first development framework.
• Academic or Practical Baseline: Bachelor's degree in Computer Science, Software Engineering, or a related technical discipline, or equivalent relevant professional experience.

Preferred Technical Multipliers & Credentials

• Professional Cloud Certifications: Active enterprise-level certifications in AWS, such as AWS Certified Solutions Architect (Professional), AWS Certified DevOps Engineer (Professional), or a Certified Kubernetes Administrator (CKA) credential.
• Large-Scale Infrastructure Footprint: Practical experience architecting and managing high-concurrency, mission-critical enterprise systems serving large user volumes.

Key Responsibilities

1. High-Fidelity Infrastructure Evaluation & LLM Alignment

• Critically review, score, and optimize simulated cloud architectures, server configurations, and deployment strategies generated by frontier AI models.
• Evaluate AI-generated configuration scripts (Terraform, CloudFormation, Kubernetes Manifests) for framework compliance, syntax accuracy, security gaps, and adherence to modern DevOps principles.
• Construct clear, step-by-step logic breakdowns and structured infrastructure refactorings to show AI models how to resolve complex container scaling errors, network isolation gaps, or deployment failures.
• Build advanced, multi-tiered deployment scenarios containing deliberately injected system design flaws to stress-test the model’s cloud debugging capabilities.

2. Distributed Architecture Design & Prototyping

• Architect and implement clean, scalable microservices-based solutions on AWS within simulated spaces to establish pristine baseline training assets.
• Design and maintain robust cluster topologies using Docker and Kubernetes, validating that ingress rules, service meshes, and volume mounts match industry security standards.
• Collaborate with internal software engineers, DevOps leads, and AI data scientists to translate business enterprise goals into highly accurate technical data models.

3. Technical Documentation & Best Practices Governance

• Write comprehensive, human-readable documentation explaining complex architectural decisions, required validation formulas, and system configuration rules.
• Mentor distributed stakeholders on cloud architecture best practices, emerging container trends, and optimal cloud cost-efficiency methods.
• Contribute detailed infrastructure quality guidelines, cloud-native policy maps, and networking security sheets to our growing technical training database.

Core Competencies & Cloud Knowledge Areas

• Distributed Topology Design: Deep operational understanding of microservices-based patterns, including service discovery, load balancing, API gateways, and event-driven communication.
• Orchestration Mechanics: Total fluency in Kubernetes object life cycles, handling stateful vs. stateless applications, pod auto-scaling, network policies, and container security hardening.
• Cloud Infrastructure Governance: Complete dedication to the AWS Well-Architected Framework, incorporating ironclad security, reliability, operational excellence, and cost optimization rules directly into training exercises.
• Advanced System Diagnostics: The analytical focus to step through complex multi-region configurations, accurately pinpointing subtle network bottlenecks, IAM configuration errors, or container orchestration race conditions.
• Asynchronous System Articulation: Strong technical writing habits, with the ability to clearly explain why a particular infrastructure setup fails or runs inefficiently under enterprise scaling constraints.

Expected Outputs & Deliverables

• Complete, error-free infrastructure-as-code templates and Kubernetes manifest files that validate perfectly against strict syntax rules.
• Detailed architectural reviews, grading summaries, and written system corrections evaluating AI-generated cloud deployments.
• Custom-designed container orchestration challenges, deployment mockups, and microservices topologies built to evaluate model performance boundaries.
• High-quality system training datasets and structured architectural files uploaded directly through the micro1 expert dashboard.

Skills Required:

• Computer / Software / It / Data