Full Stack Engineer

Proxion is a New York based company building AI training data and evaluation systems for finance. We work with expert generated data, model evaluations, and internal workflows used to measure and improve AI performance on complex financial tasks.

We are hiring a Full Stack Engineer to work from our Yerevan office. This is a hands on role with significant ownership, building and maintaining the systems that support our expert network, project workflows, quality control, and AI evaluation work.

You will be expected to work independently, make technical decisions, and take full ownership of the product from development through deployment and ongoing improvement. Finance experience is not required.

Build and maintain backend services using Python and FastAPI or Django

Design PostgreSQL schemas, APIs, authentication, permissions, and internal workflows

Build functional internal dashboards and tools using React, Next.js, and TypeScript

Integrate OpenAI, Anthropic, and other LLM APIs

Build background jobs, data pipelines, validation systems, and automated workflows

Deploy and maintain production systems using Docker and cloud infrastructure

Implement logging, backups, access controls, and audit trails

Own features from architecture through deployment and ongoing improvement

Strong Python experience, ideally with FastAPI or Django

Strong PostgreSQL and SQL skills

Experience building REST APIs and production backend systems

React, Next.js, and TypeScript experience

Experience with Docker, Git/GitHub, and basic cloud deployment

Ability to work independently and take full ownership of the product

Experience integrating LLM APIs in production (OpenAI, Anthropic, or similar)

Full time, in person in Yerevan.

Compensation: $1,500 to $2,500 per month + equity + performance bonus , depending on experience.

You will have full ownership of the product and be expected to work independently across architecture, technical decisions, development, deployment, and ongoing improvement.

Not required: Finance experience, ML training, Kubernetes, Kafka, microservices, mobile development, or visual design.

We prefer simple, reliable systems over unnecessary complexity.