Advanced Python (concurrency, typing, performance profiling) and solid packaging/testing practices ; 10+ years
API/service design (REST/gRPC, FastAPI/Flask)
Core AI/ML skills: LLM fundamentals (prompting, RAG, embeddings), rigorous model evaluation, and MLOps (versioning, drift monitoring, reproducibility)
Applied depth in a major ML framework (PyTorch/TensorFlow or LangChain-style orchestration), including its cost/latency/failure tradeoffs
Data engineering at scale (pandas/polars/SQL/Spark judgment)
5G/telecom domain knowledge: core network architecture (5GC — AMF/SMF/UPF/etc.), protocols (HTTP/2-based SBI, PFCP, NAS/NGAP), and how these map to test scenarios (signaling, throughput, session handling)
Working knowledge of network test concepts: traffic generation, DUT-based topologies, KPIs (latency, throughput, call/session success rate)
End-to-end system design ownership, from pipeline architecture through production monitoring and cost/security awareness
Technical leadership: setting direction without formal authority, mentoring, and communicating ML tradeoffs to non-technical stakeholders
Cloud platform depth (AWS/GCP/Azure) and IaC (Terraform)
ML-specific CI/CD (artifact promotion, reproducible training pipelines)
Containers/orchestration (Docker/Kubernetes) for serving
Low-level performance work (C-extensions, Cython/pybind11)
Contract/integration testing across service boundaries
