Support the implementation of AIOps strategies for automating IT operations using Azure Monitor, Azure Log Analytics, and AI-driven alerting.
Assist in deploying Azure-based observability solutions (Azure Monitor, Application Insights, Azure Synapse for log analytics, and Azure Data Explorer) to enhance real-time system performance monitoring.
Enable AI-driven anomaly detection and root cause analysis (RCA) by collaborating with data science teams using Azure Machine Learning (Azure ML) and AI-powered log analytics.
Contribute to developing self-healing and auto-remediation mechanisms using Azure Logic Apps, Azure Functions, and Power Automate to proactively resolve system issues.
Support ML lifecycle automation using Azure ML, Azure DevOps, and Azure Pipelines for CI/CD of ML models.
Assist in deploying scalable ML models with Azure Kubernetes Service (AKS), Azure Machine Learning Compute, and Azure Container Instances.
Automate feature engineering, model versioning, and drift detection using Azure ML Pipelines and MLflow.
Optimize ML workflows with Azure Data Factory, Azure Databricks, and Azure Synapse Analytics for data preparation and ETL/ELT automation.
Implement basic monitoring and explainability for ML models using Azure Responsible AI Dashboard and InterpretML.
Collaborate with Data Science, DevOps, CloudOps, and SRE teams to align AIOps/MLOps strategies with enterprise IT goals.
Work closely with business stakeholders and IT leadership to implement AI-driven insights and automation to enhance operational decision-making.
Track and report AI/ML operational KPIs, such as model accuracy, latency, and infrastructure efficiency.
Assist in coordinating with cross-functional teams to maintain system performance and ensure operational resilience.
Support the implementation of AI ethics, bias mitigation, and responsible AI practices using Azure Responsible AI Toolkits.
Ensure adherence to Azure Information Protection (AIP), Role-Based Access Control (RBAC), and data security policies.
Assist in developing risk management strategies for AI-driven operational automation in Azure environments.
Prepare and present program updates, risk assessments, and AIOps/MLOps maturity progress to stakeholders as needed.
Support efforts to attract and build a diverse, high-performing team to meet current and future business objectives.
Help remove barriers to agility and enable the team to adapt quickly to shifting priorities without losing productivity.
Contribute to developing the appropriate organizational structure, resource plans, and culture to support business goals.
Leverage technical and operational expertise in cloud and high-performance computing to understand business requirements and earn trust with stakeholders.
Qualifications
5+ years of technology work experience in a global organization, preferably in CPG or a similar industry.
5+ years of experience in the Data & Analytics field, with exposure to AI/ML operations and cloud-based platforms.
5+ years of experience working within cross-functional IT or data operations teams.
2+ years of experience in a leadership or team coordination role within an operational or support environment.
Experience in AI/ML pipeline operations, observability, and automation across platforms such as Azure, AWS, and GCP.
Excellent Communication: Ability to convey technical concepts to diverse audiences and empathize with stakeholders while maintaining confidence.
Customer-Centric Approach: Strong focus on delivering the right customer experience by advocating for customer needs and ensuring issue resolution.
Problem Ownership & Accountability: Proactive mindset to take ownership, drive outcomes, and ensure customer satisfaction.
Growth Mindset: Willingness and ability to adapt and learn new technologies and methodologies in a fast-paced, evolving environment.
Operational Excellence: Experience in managing and improving large-scale operational services with a focus on scalability and reliability.
Site Reliability & Automation: Understanding of SRE principles, automated remediation, and operational efficiencies.
Cross-Functional Collaboration: Ability to build strong relationships with internal and external stakeholders through trust and collaboration.
Familiarity with CI/CD processes, data pipeline management, and self-healing automation frameworks.
Strong understanding of data acquisition, data catalogs, data standards, and data management tools.
Knowledge of master data management concepts, data governance, and analytics.
