AI (GenAI) Engineer

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