Contract Systems Architect – AI, AWS, Consumer Platforms

MarcoPolo Learning

. Assess product requirements, existing architecture, and infrastructure; identify risks, bottlenecks, and priorities .

Posted 11/3/2026contractRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing AWS architectures for consumer applications, integrating AI workflows, and ensuring robust security measures. Proficient in backend engineering with a focus on Python and TypeScript/Node.js, alongside a strong understanding of infrastructure as code and production software management.

Highest-signal resume keywords

AWS Architecture DesignAI Workflow IntegrationBackend Engineering in PythonInfrastructure as CodeProduction Software Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AWSPythonTypeScriptNode.jsRelational DatabasesCachingMessagingContainersServerless SystemsAI Security Risks

Soft Skills

Clear Written CommunicationReliable Project Estimation

Tools & Technologies

Amazon BedrockDirect Model-Provider APIsAgent FrameworksMCP IntegrationsRetrieval SystemsVector SearchMultimodal AIModel RoutingInference OptimizationCost Measurement

Industry Keywords

Systems ArchitectStaff EngineerPrincipal EngineerConsumer ApplicationsAI ModelsProduction ControlsDeployment PipelinesHuman ApprovalTraffic SpikesUser Data Protection

Tech Stack

Tools & technologies

AWSJavaScriptNode.jsPythonTypeScript

About the role

Key responsibilities & impact

Assess product requirements, existing architecture, and infrastructure; identify risks, bottlenecks, and priorities
Design AWS architectures for consumer applications, including APIs, compute, storage, databases, caching, queues, and asynchronous processing
Evaluate AI models and providers against representative workloads, balancing output quality, latency, reliability, privacy, and cost
Design retrieval-augmented generation, structured outputs, tool use, and agent orchestration capabilities
Establish production controls for AI workflows, including evaluations, tracing, execution limits, retries, fallbacks, and human approval
Address authentication, authorization, user data protection, traffic spikes, abuse prevention, and responsive user experiences
Define infrastructure as code, deployment pipelines, environment separation, monitoring, backup, and recovery practices
Partner with engineers on implementation, review critical code and infrastructure changes, and validate assumptions through prototypes or load testing
Document decisions and provide clear handoff for independent team operation and extension
Deliver architecture assessments, prioritized recommendations, target architectures, implementation plans, prototypes or production components, infrastructure code, deployment configuration, runbooks, benchmarks, and handoff documentation

Requirements

What you’ll need

Strong track record as a Systems Architect, Staff/Principal Engineer, or technical leader building and operating production software
Deep hands-on AWS experience and ability to explain service choices and architectural tradeoffs
Experience shipping consumer applications and managing growth, availability, performance, and infrastructure costs
Production experience integrating LLMs and building AI workflows beyond demonstrations or proofs of concept
Current knowledge of leading commercial and open-weight models, with a repeatable approach to evaluating new releases
Strong backend engineering skills in Python, TypeScript/Node.js, or comparable languages
Experience with relational databases, caching, messaging, containers or serverless systems, and infrastructure as code
Practical understanding of AI security risks, including prompt injection, sensitive data exposure, and excessive tool permissions
Clear written communication, reliable project estimation, and experience delivering independently within an agreed scope
North American time zones preferred; candidates elsewhere must provide suitable working-hour overlap
Useful additional experience with early-stage startups, Amazon Bedrock, direct model-provider APIs, agent frameworks, MCP integrations, retrieval systems, vector search, personalization, multimodal AI, model routing, inference optimization, and cost measurement

Benefits

Comp & perks

Project-based independent contractor engagement
Hourly or fixed project fee based on scope and experience
Flexible schedule with agreed overlap
Follow-on projects may be available