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# Full Stack AI Engineer

AI Summary This contract role is for a senior full-stack AI engineer building production software for healthcare practices. The engineer will own features end to end, including problem definition, architecture, implementation, deployment, monitoring, and incident response. Core work includes AI agents and workflow automation for clinical operations, with strong requirements around PHI security and auditability. The stack spans Python, TypeScript, React, React Native, PostgreSQL, AWS, Terraform, Temporal, and LLM tooling such as Claude Code, Codex, MCP integrations, and evaluations.[Apply for this job](#job-application)[View company](https://jobicy.com/company/3y-health.md)ShareRemote from[Argentina](https://jobicy.com/job-region/argentina.md)SalaryUndisclosedDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentContractExperienceSeniorPublished11 Nov 2026Apply before11 Dec 2026Listing views46Application actions2Application toolkit

## Make your next move.

Prepare your resume, explore your fit, and draft a cover letter for this opportunity.

Opportunity details

## About this role.

### About [3Y Health](https://jobicy.com/company/3y-health.md)

3Y Health is building the operating system for healthcare practices, an AI-native platform that helps clinicians start, run and grow their practice. We work as a managed services organization (MSO): along with the software, our operations team runs the back office for the clinics we partner with, including billing, insurance and compliance. We currently serve applied behavior analysis (ABA) therapy practices and are expanding into speech, occupational and physical therapy, and psychiatry.

### About the role

Members of Technical Staff own features end to end. You’ll work with the clinicians and operators who use what we build, scope the problem, design and build the solution across the stack, deploy it and follow how it performs in production. Our engineering team is flat, with no sub-teams, so you’ll work across the whole platform. We build with AI coding agents every day and expect you to do the same.

### What you’ll do

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Design and build AI agents and automated workflows for clinic operations, such as insurance benefits verification, prior authorizations, patient intake, claims and clinical documentation

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Build features for clinicians, patients and families, and our internal operations team, on web and mobile

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Take ambiguous problems from 0 to 1, working with operators and clinicians to define the problem, write the spec and ship a first version

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Design the services, data models and APIs behind your features and manage the infrastructure as code

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Own your services in production: deployments, monitoring and incident response

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Write evals for model-facing features and track the metrics of the workflows you ship

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Work with protected health information (PHI) safely, with least-privilege access, no PHI in logs and auditable changes

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Build and maintain the skills, plugins, MCP integrations and review bots our engineers use with Claude Code and Codex

### Our stack

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Backend: Python (our primary language for new services), Kotlin with Hasura (GraphQL) on our established platform, plus TypeScript (NestJS) and some Go

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Frontend and mobile: React, Next.js, React Native

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Data: PostgreSQL (including pgvector), Snowflake

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Workflows and contracts: Temporal, gRPC/Protobuf

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Infrastructure: AWS (ECS Fargate, RDS), Terraform, GitHub Actions, Datadog, LaunchDarkly

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AI: Claude and OpenAI models, Claude Code and Codex, with our own skills, plugins, MCP integrations and an AI code-review bot

### Must-haves

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5+ years of experience building and shipping production software

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Recent, hands-on experience using or building AI harnesses: the layer around a model or coding agent (project instructions, tools and MCP servers, skills, hooks, subagents, evals), not only calls to a model API. You can show what you set up or built.

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At least one personal project you built and published on the web, live and usable by anyone, ideally a SaaS with payments working (e.g. Stripe). Share the link when you apply.

### What we’re looking for

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A track record of owning products or features end to end, from an unclear problem to a system people depend on

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Strong full-stack skills in Python and TypeScript, with experience across frontend, backend, databases and cloud infrastructure

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Comfort working in a large, established codebase

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Hands-on experience building with LLMs, including tool use, agent orchestration, structured outputs, retrieval and evals (LangGraph, an agents SDK or similar)

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Daily use of AI coding agents such as Claude Code, Codex or Cursor. You know how to set them up for a codebase (project instructions, skills, MCP servers, hooks, subagents), run several tasks in parallel and review their output carefully. If you haven’t heard of Claude Code, this role is likely not a fit.

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Good product sense and the ability to make solid UI decisions without a dedicated designer, using our component library

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High agency: you unblock yourself, look for context and ask questions when you need to

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Clear written communication. The team is distributed across time zones and most decisions happen in specs, PRs and tickets.

### Nice to have

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Experience in healthcare or another regulated industry (HIPAA, payer and claims workflows, EHR integrations, HL7/FHIR)

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Temporal or another durable workflow engine

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React Native

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Running LLM evaluations or safety checks on model output in production

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Having built your own agent harness, MCP server or plugin for a coding agent

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Open-source work or internal tools that other people use

If you meet all the must-haves and most of the rest, we’d like to hear from you, even if you don’t check every other box.

Show more

[Apply now >](https://jobicy.com/jobs/161438-full-stack-ai-engineer.md)

>  Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md)

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![Upload CV](data:image/svg+xml;base64,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)

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## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

5/5EasyHard

### Pace & Pressure

5/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

4/5IndependentCollaborative

AI insightThe role combines senior full-stack ownership, production AI-agent engineering, infrastructure operations, and healthcare compliance responsibilities. Candidates must independently turn ambiguous workflows into reliable systems while safely handling regulated data.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate$135,000US market range (per year)$115k–$165k0$181.50k

AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer base-pay range is stated. The annual period is inferred from common compensation conventions for senior software engineering roles; the figures are estimated USD market compensation, not verified employer pay.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Python](https://jobicy.com/jobs?search_keywords=Python.md)[TypeScript](https://jobicy.com/jobs?search_keywords=TypeScript.md)[React](https://jobicy.com/jobs?search_keywords=React.md)[Next.js](https://jobicy.com/jobs?search_keywords=Next.js.md)[React Native](https://jobicy.com/jobs?search_keywords=React%20Native.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[Terraform](https://jobicy.com/jobs?search_keywords=Terraform.md)[PostgreSQL](https://jobicy.com/jobs?search_keywords=PostgreSQL.md)[GraphQL](https://jobicy.com/jobs?search_keywords=GraphQL.md)[Temporal](https://jobicy.com/jobs?search_keywords=Temporal.md)[LLM agent orchestration](https://jobicy.com/jobs?search_keywords=LLM%20agent%20orchestration.md)[MCP integrations](https://jobicy.com/jobs?search_keywords=MCP%20integrations.md)[LLM evaluations](https://jobicy.com/jobs?search_keywords=LLM%20evaluations.md)[Healthcare compliance](https://jobicy.com/jobs?search_keywords=Healthcare%20compliance.md)[Production incident response](https://jobicy.com/jobs?search_keywords=Production%20incident%20response.md)

Sample interview questionsDescribe an AI-powered workflow you took from an ambiguous business problem to production.I would explain how I clarified the user and operational requirements, defined measurable success criteria, designed the workflow and interfaces, implemented safeguards and observability, launched incrementally, and used production metrics and feedback to improve it.

How would you design an AI agent for insurance benefits verification while protecting PHI?

I would minimize data access, use least-privilege service identities, avoid PHI in logs and prompts where possible, encrypt data in transit and at rest, maintain audit trails, validate tool inputs and outputs, and establish clear human-review paths for uncertain cases.

What does a robust LLM evaluation strategy look like for a production workflow?

It starts with representative versioned test cases and explicit quality, safety, latency, and cost metrics. I would run offline evaluations before release, monitor live outcomes after deployment, investigate failures through traceable data, and continuously add difficult cases to the evaluation set.

How have you configured AI coding agents for use in a real codebase?

I would describe maintaining repository instructions, scoped skills, tool permissions, MCP integrations, hooks, and test commands. I would also emphasize task decomposition, parallel execution where safe, and rigorous review of generated changes through tests, security checks, and code review.

How do you approach owning a service after deployment?

I define service-level indicators, dashboards, structured logging without sensitive data, alerts, deployment rollback procedures, and incident runbooks. I also review failures and operational metrics regularly to prioritize reliability and performance improvements.

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