AI Software Engineer

Quick Facts

AI Software Engineer role focused on building and improving AI products using agents, LLMs, and RAG.

Description

You will develop an AI workspace (documents, organizational knowledge, digital twins, and agents), an AI model/tool access platform with permissions, security, cost control, and auditing, and deployment support software for AI processes and audit evidence. The work spans from backend APIs and data modeling to a functional user interface, including code review of AI-generated changes and end-to-end troubleshooting. You’ll own correctness, test coverage, and the reliability of LLM-based integrations.

Responsibilities

• Develop features from API and data model to a working user interface using Codex.
• Analyze requirements, define acceptance criteria, and prepare plans/instructions/context for AI coding agents.
• Implement and integrate LLM applications, RAG, AI agents, and MCP services with checks for state, permissions, and costs.
• Build business process support, rules, forms, approvals, document handling, and mechanisms for data isolation, access control, secure integrations, and system action logging.
• Review and fix AI-generated code; run automated tests; evaluate answer quality and verify changes in the browser.
• Diagnose bugs, refactor, update dependencies, document changes, and collaborate on architecture and deployments.

Requirements

• Minimum 3 years of Python development and practical experience building LLM-based applications.
• Practical knowledge of web APIs (e.g., FastAPI), asynchronous programming, SQL, transactions, and database migrations.
• Ability to work with Codex (or similar agents): split tasks, prepare context, assess changes, and debug independently.
• Knowledge of JavaScript or TypeScript, HTML, and CSS to develop and validate the UI (forms, application state, API integrations, error handling).
• Understanding of RAG, vector search, agent tool calls, and management of state and user approvals.
• Ability to design access control, protect secrets, isolate data, and understand LLM-related threats.
• Practical experience with Git, automated tests, Docker, and basic Linux; ability to diagnose issues from logs.
• Capability to translate user needs into tasks and acceptance criteria, with accountability for feature quality.

Benefits

• Clear and stable employment forms based on your preferences.
• Work on in-house AI products and projects for large public and private institutions.
• Internal training and co-funding for courses aligned with your career and interests.
• Opportunity for professional growth, company laptop and phone, and a friendly work atmosphere.

Additional Benefits

• Private health insurance, additional liability insurance, and periodic preventive care.
• Referral bonus program.
• No dress code; snacks in the office and specialty coffee and tea.