Mandatory requirements:

– Experience with PyTorch and model optimization for production

– Compilation of ML models into native code (ONNX, TorchScript, experience with compiling Python into binaries)

– Working with GPU (CUDA, inference optimization on graphics accelerators)

– Experience deploying models in a closed circuit without Internet access

– Containerization of ML solutions (Docker, understanding of security requirements)

– Experience with quantized models for edge computing

– Understanding of working with time series and streaming data

– Experience in classifying network traffic or similar real-time tasks

– Working with large volumes of data (terabits of traffic)

Technical stack:

– Python (with the ability to compile into binaries via Cython)

– C/C++ for low-level optimization

– Experience with LangGraph/Pydantic AI or similar for building agent systems

– Vector databases (Weaviate, Qdrant)

– Ollama or similar for local deployment of LLM

– Hadoop, Hive, Spark for processing big data

– Kafka for working with streaming data

Additional requirements:

– Readiness to work with closed systems without external dependencies

– Understanding of security requirements in the telecom industry

– Experience in international projects (the system is deployed in different countries)

– Knowledge of English (documentation, communication with DPI library vendors)

– Experience in managing data science projects

– Networking concepts (TCP/IP, RADIUS, GTP)

– Linux system configuration

– Git/GitHub for version control

A plus:

– Experience with DPI systems (Sandvine, Procera, Allot)

– Familiarity with DPDK and high-performance packet processing

– Experience with Rohde & Schwarz or Enea Qosmos SDK

– Understanding of network protocols and the structure of Internet traffic

– Experience with Dynamic Content Classification

– Working with Quality of Experience (QoE) models

– Network Intelligence and high-precision traffic classification

Education:

– BE/ME in Computer Science or Engineering

– 2+ years of practical experience in data science/ML