AI Engineer

Technologies we use

About the project

This is how we organize our work

This is how we work

Team members

Your responsibilities

• Design, build, and maintain AI-powered services and backend applications.
• Develop data pipelines for ingesting, validating, normalizing, enriching, and transforming financial data from multiple sources.
• Build and optimize LLM-based applications, AI agents, and Retrieval-Augmented Generation (RAG) solutions and orchestration workflows.
• Develop APIs and services exposing AI-ready financial data objects.
• Design prompt engineering strategies, model evaluation, benchmarking and observability.
• Evaluate and optimize AI solutions for accuracy, reliability, latency, token usage and cost.
• Collaborate with Product and Engineering teams to translate business requirements into scalable technical solutions.
• Contribute to designing scalable cloud-native architecture and backend services.
• Write clean, maintainable, and well-tested production code.
• Participate in code reviews, architecture discussions, and continuous improvement initiatives.
• Support deployment, monitoring, troubleshooting, and performance optimization of AI applications.
• Continuously evaluate new models, AI technologies, frameworks, and best practices.

Our requirements

• 3+ years of professional software engineering experience, with strong background in backend, data or machine learning engineering.
• Strong Python programming skills.
• Experience building production-grade backend applications and APIs.
• Hands-on experience building applications with modern LLMs, including AI agents, agentic workflows, RAG architectures and orchestration frameworks (e.g. LangChain, LangGraph, LlamaIndex, Semantic Kernel).
• Experience with prompt engineering, model evaluation and testing AI applications
• Experience working with relational databases (PostgreSQL) and vector databases (e.g. pgvector, Qdrant, Pinecone).
• Understanding of modern data engineering concepts and ETL/data pipelines.
• Experience working with cloud platforms (preferably Azure).
• Familiarity with Docker, Kubernetes, Git, and CI/CD.
• Knowledge of REST APIs and distributed systems.
• Strong problem-solving skills and ability to work independently.
• Very good English communication skills.

Optional

• Experience in FinTech, WealthTech, banking, or financial data.
• Experience with MLOps, model monitoring, evaluation, and observability tools (e.g. Langfuse, MLflow).
• Knowledge of Azure AI, Azure OpenAI, Databricks, Microsoft Fabric, or similar technologies.
• Experience with event-driven architectures and messaging systems.
• Understanding of AI governance, security, privacy, and regulated environments.
• Experience working in startups or building products from scratch.

What we offer

• Mission before ego.
• Builders over managers.
• Small, elite teams.
• Q-SOCS™ – Quality - Speed, Ownership, Challenge, Solutions
• Speed over bureaucracy.
• Ownership and accountability.
• The best idea wins-not the highest title.
• High-quality solutions delivered

What we're looking for?