Description

You will engineer and deploy production-grade generative AI solutions.

Responsibilities

• Build internal assistants or copilots for network operations, triage, or RCA using KPIs, alarms, tickets, and documentation.
• Develop agentic workflows and orchestration using function/tool calling, multi-step chains, and retrievers/guardrails to automate diagnosis and action proposals.
• Implement MLOps/LLMOps practices, including CI/CD for pipelines, versioning (model/prompt/knowledge), and drift monitoring.
• Manage data and AI solutions within the Azure ecosystem, leveraging Azure AI Search, Azure Event Hubs, and AKS.
• Ensure data quality and reliability by building testable data transformations on large-scale telemetry datasets.

Required Skills

• 3–5 years of experience in AI/ML engineering, data engineering, or applied data science delivering production solutions.
• Strong proficiency in Python and SQL, with expertise in working with large-scale time-series datasets.
• Hands-on experience with Azure Machine Learning, Azure AI Services/OpenAI, and Azure Data Factory/Synapse.
• Experience with containerized deployments using Kubernetes (AKS).
• Familiarity with GenAI frameworks (e.g., LangChain/LlamaIndex) and vector databases.
• Proficiency with version control (Git) and CI/CD tooling (Azure DevOps).
• Experience with automated testing and implementing prompt/unit regression testing.
• Understanding of data lakehouse patterns using Delta Lake.
• Familiarity with observability practices, including OpenTelemetry.

Key Skills

Education

Any Gradute