AI Engineer

Main Responsibilities

1. Research and implementation of AI models, especially:

○ LLM (text processing, reasoning, workflow agent)

○ Computer Vision (object detection, OCR enhancement)

○ Retrieval and indexing (using Qdrant, Elasticsearch, or other vector DB)

1. Building and developing backend APIs based on FastAPI for AI model integration.

1. Supporting internal product development.

1. Participating in custom client projects, specifically for proof of concept or AI-based prototypes.

1. Conducting exploration of new tools, frameworks, and methods in the AI field to support the R&D roadmap.

1. Coordinating with other R&D teams (deployment, data, and QA) for stable and documented system integration.

1. Writing technical documentation for developed models, APIs, and workflows.

Minimum Qualifications

1 - 2 years of experience as an AI Engineer

Holding a Bachelor's degree (S1) or Applied Bachelor's degree (D4)

Hard Skills:

1. Python – capable of writing modular, neat, and readable code.

1. Familiar with FastAPI or similar backend frameworks (Flask, Django REST).

1. Basic knowledge of using and integrating open-source AI models, having used at least one of:

○ HuggingFace Transformers

○ LangChain / LangGraph

○ OpenCV / YOLO / Donut / PaddleOCR

○ Ollama or LLM inference frameworks

1. Basic knowledge of databases (MongoDB, PostgreSQL, or others).

1. Familiar with Docker and container deployment concepts (expert level not mandatory).

1. Able to read technical documentation in English well.

Nice to Have:

• Experience creating small AI projects (thesis, freelance, or personal) with end-to-end implementation.
• Familiar with Vector Databases (Qdrant, Chroma, Weaviate).

• Understanding basic concepts of prompt engineering, agentic AI, or AI workflow orchestration.
• Understanding basic concepts of API design and integration between microservices.