Contract Systems Architect — AI, AWS & Consumer Platforms

Contract Systems Architect — AI, AWS & Consumer Platforms

Engagement: Project-based independent contractor
Location: Remote, worldwide; North American time zones preferred
Schedule: Flexible, with agreed overlap with our team
Compensation: Hourly or fixed project fee, based on scope and experience

We’re an AI-focused consumer technology startup building products designed to reach a large user base. We’re looking for an experienced Systems Architect to help us make practical technical decisions, deliver reliable AI capabilities, and establish infrastructure that can grow with the business.

This is a project-based contract role. Each engagement will have an agreed scope, timeline, deliverables, and acceptance criteria. Follow-on projects may be available as our needs evolve.

You’ll work directly with founders and engineers to translate product goals into secure, scalable, and cost-effective systems. Your work may span architecture reviews, AWS infrastructure, AI model integration, agentic workflows, and production readiness.

You should be comfortable moving from architecture diagrams to code, prototypes, infrastructure configuration, and technical reviews. We value clear judgment: knowing what to build now, what to defer, and when a simpler design will serve the business better.

• Assess our 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 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 such as 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 key assumptions through prototypes or load testing.
• Document decisions and provide a clear handoff so our team can operate and extend the systems independently.

• A strong track record as a Systems Architect, Staff/Principal Engineer, or technical leader building and operating production software.
• Deep hands-on AWS experience and the 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.

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

Depending on the engagement, deliverables may include:

• An architecture assessment and prioritized recommendations.
• A target architecture, documented tradeoffs, and phased implementation plan.
• Working prototypes or production components for critical capabilities.
• Infrastructure code, deployment configuration, and operational runbooks.
• Performance, reliability, AI quality, and cost benchmarks.
• Engineering handoff documentation and knowledge-transfer sessions.

Success means delivering systems that meet agreed requirements, fit our stage and budget, and can be maintained by our team.

Candidates worldwide are welcome. North American time zones are preferred, but we’re open to candidates elsewhere who can provide suitable working-hour overlap.

Originally posted on Himalayas