Applied AI Engineer: Build Production AI Agents for Finance

Moss; Poland; On-site; PLN 260,000 - 420,000; Full time

Benefits offered by this job

Equity

Work from abroad program

Learning & Development budget

Local benefits

Job summary

Moss, the Finance AI platform, is hiring an Applied AI Engineer to design, build and operate AI agents that automate complex finance workflows. You will own end-to-end features from architecture to production, integrating with backend services, data models and product workflows.

You will collaborate with data science to productionize ML models, apply context engineering techniques, and ensure security, privacy and observability in production deployments.

Qualifications

• Seasoned software engineer with production-grade experience.
• Proficient in Python and/or Java with backend/API work.
• Experience building end-to-end agent or LLM-powered product capability.
• Experience integrating agents into a broader product and backend architecture.
• Experience evaluating agents or non-deterministic AI systems.
• Understanding data security, permissions and privacy.
• Hands-on with prompt engineering, context engineering and orchestration.

Responsibilities

• Build and ship AI agents that automate complex finance workflows.
• Evaluate agent performance with systematic tests and metrics.
• Develop AI application architecture using context-engineering techniques.
• Integrate ML capabilities and productionize ML models into the product.

Skills

Python

Java

Backend services

REST APIs

Agent orchestration

Context engineering

LLM integration

Production engineering

Prompt engineering

Security & privacy

Tools

Google ADK

LangGraph

LangChain

LlamaIndex

BigQuery

Kafka

Job description

Moss, the Finance AI platform, is hiring an Applied AI Engineer to design, build and operate AI agents that automate complex finance workflows. You will own end-to-end features from architecture to production, integrating with backend services, data models and product workflows.

You will collaborate with data science to productionize ML models, apply context engineering techniques, and ensure security, privacy and observability in production deployments.