Contract Systems Architect — AI, AWS & Consumer Platforms

MarcoPolo Learning

7d•Remote

About The Position

This project-based independent contractor role supports an AI-focused consumer technology startup. The Systems Architect will work directly with founders and engineers to turn product goals into secure, scalable, and cost-effective systems. Engagements have agreed scopes, timelines, deliverables, and acceptance criteria; follow-on projects may be available as needs evolve. The work may include architecture reviews, AWS infrastructure, AI model integration, agentic workflows, and production readiness. The role spans architecture diagrams, code, prototypes, infrastructure configuration, and technical reviews. The architect is expected to exercise sound judgment about what to build immediately, what to defer, and when a simpler design is appropriate. Project deliverables may include architecture assessments and prioritized recommendations; target architectures, documented tradeoffs, and phased implementation plans; working prototypes or production components; infrastructure code, deployment configuration, and operational runbooks; performance, reliability, AI quality, and cost benchmarks; and handoff documentation and knowledge-transfer sessions. Success means delivering systems that meet agreed requirements, fit the company’s stage and budget, and can be maintained by its team. The role is remote worldwide, with North American time zones preferred and suitable working-hour overlap required. Schedule is flexible, and compensation is hourly or a fixed project fee based on scope and experience.

Requirements

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.

Nice To Haves

Experience with early-stage startups and small engineering teams.
Experience with Amazon Bedrock and direct model-provider APIs.
Experience with agent frameworks and Model Context Protocol (MCP) integrations.
Experience with retrieval systems, vector search, personalization, or multimodal AI.
Experience with model routing, inference optimization, and cost measurement per user or completed task.

Responsibilities

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 using representative workloads, balancing output quality, latency, reliability, privacy, and cost.
Design AI capabilities such as retrieval-augmented generation, structured outputs, tool use, and agent orchestration where they improve the product.
Establish production controls for AI workflows, including evaluations, tracing, execution limits, retries, fallbacks, and appropriate human approval.
Address consumer platform needs, including 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 a handoff that enables the team to operate and extend the systems independently.
Deliver project-specific outputs such as architecture assessments, target architectures, prototypes or production components, infrastructure code, operational runbooks, benchmarks, and handoff materials.