1,200,000 to 2,000,000 KZT Gross per month · Almaty / Office
BI Group is one of the largest developers in Central Asia. BI Group is expanding its internal AI engineering team to build business assistants, document and data automation, and production AI systems.
BI Group is one of the largest developers in Central Asia. BI Group is expanding its internal AI engineering team to build business assistants, document and data automation, and production AI systems.
1,200,000 to 2,000,000 KZT Gross per month · Almaty / Office
• Design, build, and ship AI agents and LLM-based tools, data pipelines, FastAPI APIs, dashboards, internal integrations, and monitoring.
• Translate business problems into technical scope and communicate methods, results, and limitations in plain language.
• Apply classical ML for scoring and forecasting, maintain data quality and freshness, and monitor production quality and drift.
• Contribute to engineering standards, collaborate with analysts and data engineers, and mentor junior engineers.
• Translate business problems into technical scope and communicate methods, results, and limitations in plain language.
• Apply classical ML for scoring and forecasting, maintain data quality and freshness, and monitor production quality and drift.
• Contribute to engineering standards, collaborate with analysts and data engineers, and mentor junior engineers.
• 4+ years of commercial Python development, including 2+ years in ML or applied AI.
• LLM agents with LangGraph, RAG with vector databases such as Qdrant or Chroma, prompt engineering, and evaluation or observability with LangSmith, Langfuse, or similar tools.
• Production AI/ML delivery, strong SQL and PostgreSQL, including data marts, materialized views, and query optimization.
• Business stakeholder communication, code review, testing, Git, reproducible experiments, and documentation.
• LLM agents with LangGraph, RAG with vector databases such as Qdrant or Chroma, prompt engineering, and evaluation or observability with LangSmith, Langfuse, or similar tools.
• Production AI/ML delivery, strong SQL and PostgreSQL, including data marts, materialized views, and query optimization.
• Business stakeholder communication, code review, testing, Git, reproducible experiments, and documentation.
• Classical ML with pandas, scikit-learn, gradient boosting, leak-free validation, and quality metrics including AUC, precision, recall, and calibration; Airflow or similar orchestration tools; Docker/Kubernetes; construction, industrial, or real estate analytics, or 1C data.
• Fluent Russian and English sufficient to read technical documentation.
• Fluent Russian and English sufficient to read technical documentation.