Client Job Title: Azure AI

UST Job Title: Lead I - DevOps Engineering

Who we are:

At UST, we help the world s best organizations grow and succeed through transformation. Bringing together the right talent, tools, and ideas, we work with our client to co-create lasting change. Together, with over 30,000 employees in over 25 countries, we build for boundless impact touching billions of lives in the process. Visit us at UST.com.

The Opportunity:

Summary:

We are seeking a highly skilled Azure AI Engineer with expertise in stress testing, AI/ML analytics, and cloud-based data engineering to build an intelligent firmware reliability pipeline. You will design and execute stress tests on firmware before production release, analyse multi-dimensional telemetry data (memory, power, thermal), and develop predictive models to identify failure signatures and optimize firmware performance. This role requires a unique blend of embedded systems knowledge, statistical/ML expertise, and Azure data engineering capabilities.

Key Responsibilities:

·       Machine Learning & Predictive Analytics

·       Design and implement ML models (Azure ML, Isolation Forest, LSTM autoencoders) for anomaly detection, failure prediction, and root cause analysis using telemetry data (e.g., memory, power, thermal metrics).

·       Develop time-series forecasting models (ARIMA, LSTM, Prophet) to anticipate performance degradation and system failures.

·       Failure prediction based on historical stress test data (Azure Anomaly Detector, Random Forest, XGBoost)

·       Apply statistical techniques (e.g., regression, hypothesis testing, ANOVA, survival analysis) to analyse telemetry trends and validate model outputs.

·       Apply hypothesis testing frameworks (A/B testing, t tests, chi square, KS tests) to validate performance changes, anomaly thresholds, and reliability impacts under different stress-test conditions.

·       Use SHAP, LIME, and other explainability tools to interpret model predictions for engineering stakeholders.

·       Integrate Azure OpenAI / Foundry tools for interactive telemetry exploration and intelligent query generation over Kusto/ADX datasets.

·       Azure Data Engineering & MLOps

·       Build and maintain scalable data pipelines using Azure Data Factory for ingesting and transforming telemetry data from JSON logs and other sources.

·       Design and optimize Kusto (ADX) schemas and queries for high-throughput telemetry analytics.

·       Develop and manage Azure ML Pipelines for automated training, evaluation, and deployment of ML models.

·       Collaboration & Domain Understanding

·       Partner with firmware and hardware teams to understand telemetry instrumentation, stress test scenarios, and failure modes.

·       Translate engineering requirements into ML problem statements and data-driven solutions.

·       Present insights and recommendations through dashboards, reports, and interactive tools (e.g., Power BI).

Required Skills & Qualifications:

·       Strong experience in machine learning, anomaly detection, time-series analysis, and statistical modelling.

·       Proficiency in Python, ML frameworks and data manipulation tools.

·       Proven experience designing and operating CI/CD pipelines in Azure DevOps for ML systems, including data validation, unit/integration tests, and staged deployments (dev staging prod).

·       Knowledge of firmware development, device drivers, or hardware validation workflows.

·       Hands-on expertise with Azure ML, Azure Data Factory, ADX/Kusto, and related cloud data services.

·       Experience with LLM-assisted analytics for pattern recognition or telemetry summarization

Skills

·       azure devops,cloud-based data engineering,firmware validation,stress testing,machine learning,anomaly detection,ai/ml analytics,