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.