About Good Outcomes

Good Outcomes is the regulatory intelligence platform for financial services. We help banks, insurers, and fintechs transform customer communications into actionable insights—predicting regulatory risk, surfacing conduct issues, and enabling outcomes-based governance at scale.

We're an AI-native company. That means we don't just build AI products—we build with AI. Claude Code is part of our daily workflow. Agentic coding patterns are standard practice. If you're the kind of engineer who's already shipping features 10x faster because you've figured out how to collaborate with LLMs effectively, we want to talk.

The Role

We need a full-stack engineer who can guide and orchestrate agents across our entire stack: React frontend, Python backend, Kubernetes infrastructure. Someone who knows how to direct AI to do the heavy lifting while maintaining architectural vision and quality. Someone who can ship a new feature end-to-end—from database schema to API endpoint to React component to Kubernetes deployment—by harnessing the right skills for each layer.

You'll work on a platform that processes thousands of customer signals daily, powers voice AI agents, and helps compliance teams make better decisions faster. Your role is less about typing code and more about steering agents, reviewing their output, and knowing when to intervene.

What You'll Work On

Frontend (React 19 + TypeScript)

• Guide agents to build and evolve our web application interfaces
• Direct component development using our library (Radix UI, TailwindCSS, Framer Motion)
• Orchestrate real-time feature implementation using WebSockets and React Query
• Build voice interfaces using LiveKit SDK for Juno, our AI agent
• Steer data visualization work with Recharts and ReactFlow

Backend (Python + FastAPI)

• Architect APIs for signal processing, classification, and analytics—then guide agents through implementation
• Design background job pipelines with Celery and direct agents to build them
• Integrate LLM capabilities using OpenAI, LangChain, and LangGraph
• Work with PostgreSQL (including pgvector for embeddings) and Redis
• Implement WebSocket endpoints for real-time updates

Infrastructure (Kubernetes + GCP)

• Manage deployments on GKE using Kustomize overlays, leveraging agents for configuration generation
• Build and maintain CI/CD pipelines with Cloud Build
• Direct agents to configure container deployments (resource limits, health checks, scaling)
• Work with Terraform for infrastructure provisioning
• Implement observability with OpenTelemetry

You Should Have

Full Stack Fluency

• Deep understanding of React/TypeScript patterns—you can review, guide, and correct agent-generated UI code
• Python backend expertise with FastAPI or similar—you know what good looks like and can steer agents toward it
• Comfortable with PostgreSQL, Redis, and async programming patterns
• Experience architecting real-time features (WebSockets, server-sent events)

Kubernetes & Infrastructure

• Experience managing applications on Kubernetes—you can direct agents to generate correct configs
• Familiarity with GCP services (or equivalent AWS/Azure experience)
• Understanding of container orchestration, networking, and observability
• Experience with CI/CD pipelines and GitOps workflows

Agentic Engineering (This is non-negotiable)

• You work daily with Claude Code, Codex, or whichever agent fits the task at hand
• You have a feel for which agent harness works best for which problem—deep reasoning, fast iteration, exploratory research, or code generation
• You continuously evolve your workflow as new tools and patterns emerge
• You bring your own agentic skills to the table as part of your contribution to the team
• You can articulate why you reach for a particular agent for a particular job
• You know when to let an agent run and when to take back the wheel

Bonus Points

• Experience with LLM integration (OpenAI API, LangChain, or similar)
• Background in financial services, regtech, or compliance
• Experience with voice/audio systems (LiveKit, WebRTC)
• Contributions to open-source projects
• Experience with multi-tenant SaaS architectures

Tech Stack

Frontend: React 19, TypeScript, TailwindCSS, Radix UI, React Query, Framer Motion, LiveKit SDK, Recharts, ReactFlow Backend: Python 3.11+, FastAPI, Celery, SQLModel/SQLAlchemy, PostgreSQL, pgvector, Redis AI/LLM: OpenAI API, LangChain, LangGraph, LangSmith Infrastructure: Kubernetes (GKE), Docker, Terraform, Kustomize, Cloud Build, Google Cloud Platform Observability: OpenTelemetry, Google Cloud Monitoring, Flower (Celery)

What "AI-Assisted" Means to Us

We're not looking for engineers who passively accept autocomplete suggestions. We want people who have fundamentally changed how they work—from writing code to orchestrating agents that write code:

• You use Claude Code, Codex, or similar daily—and you've developed intuition for which agent suits which task
• You think of yourself as a guide and reviewer, not just a coder—your job is to direct agents toward the right solution and catch when they veer off course
• You continuously evolve your flow as new capabilities emerge—what worked last month might not be your approach today
• You bring your agentic skills as part of your contribution to the team: your ability to harness AI effectively is as valuable as your technical knowledge
• You understand agent limitations and know when to intervene, correct course, or take over entirely
• You're excited about where this is all going—and you're actively shaping your own workflow to get there first

If you're still coding the way you coded in 2022, this probably isn't the right fit. If you've rebuilt your workflow around guiding agents and can't imagine going back to writing everything yourself, let's talk.

Why Good Outcomes?

• Ship fast: Small team, minimal process, maximum ownership
• AI-native culture: We practice what we preach—AI tools are embedded in everything we do
• Hard problems: Real ML systems, real-time processing, voice AI, regulatory complexity
• Impact: Help financial institutions treat customers fairly at scale
• Remote-first: Async-friendly, flexible hours, results-oriented

How to Apply

Install our application skill, then ask your agent to apply for the AI-Assisted Full Stack Engineer role.

Option 1: Via skills.sh (Recommended)

The fastest way to install:

npx skills add DataEQ/go-people-skills

This works with Claude, Codex, Cursor, Moltbot, and any other harness that supports skills.

Option 2: Claude Code Plugin Marketplace

Add the marketplace and install the plugin:

/plugin marketplace add DataEQ/go-people-skills
/plugin install apply-to-good-outcomes@go-people

Option 3: Direct Clone

git clone https://github.com/DataEQ/go-people-skills.git ~/.claude/plugins/go-people-skills

Usage

Once installed, just ask your agent to apply for this role—or run /apply to get started.

Good Outcomes is committed to building a diverse team. We encourage applications from candidates of all backgrounds.