Data Engineer - GenAI

We’re looking for an experienced AI/ML engineer to design, build, and operationalize Generative AI solutions that directly enhance network performance, diagnostics, and automation. Minimum Qualifications • Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Science, or a similar technical discipline — or equivalent real‑world experience. • 3–5 years of hands-on experience in AI/ML engineering, applied data science, or data engineering delivering production-ready systems. • Strong proficiency in Python and SQL, with proven experience processing large-scale telemetry and time‑series datasets and building maintainable, testable data pipelines. • Practical experience using Azure services for data and AI workloads, including: Azure Machine Learning, AI Services / Azure OpenAI (LLMs, GenAI models), Databricks / Spark (Delta Lake, lakehouse patterns), Data Lake Storage or Blob Storage, Azure Functions, DevOps, Git, and automated testing frameworks • Solid understanding of GenAI development best practices: • RAG workflows • Prompt engineering • Evaluation principles: accuracy, grounding, safety, response quality, latency, and cost • Strong production mindset: logging, monitoring/observability, troubleshooting, security basics (RBAC, managed identities, Key Vault, PII handling), and readiness for real-world performance scenarios (rate limits, timeouts, retries, caching). Domain Qualifications — RAN & Mobility • Solid knowledge of 4G/5G RAN fundamentals and mobility behaviors: handovers, call drops, throughput, congestion, interference, RSRP/RSRQ/SINR, PRB usage, and related KPIs. • Ability to interpret complex network issues and turn them into measurable KPIs, diagnostic flows, and actionable engineering insights (e.g., RCA paths, remediation steps, validation plans). Preferred Qualifications • Experience developing internal copilots/assistants for network operations, triage, or RCA — ideally using alarms, KPIs, tickets, and documentation with traceable evidence and citations. • Familiarity with agent-based orchestration (tool calling, multi-step workflows, retries, guardrails) for automated diagnostics or action planning. • CI/CD for ML/LLM pipelines • Drift monitoring and observability (OpenTelemetry) • Token, cost, and performance governance • Deep experience with Azure data and AI ecosystem components