Machine Learning Engineer

• - Build, train, and optimize models for predictions such as churn, fall risk signals, emergency intent classification, and behavioral patterns.
• - Implement NLP, embeddings, and LLM-based workflows for contextual understanding of transcripts, messages, and voice interactions.
• - Develop real-time scoring services integrated with Azure Event Hub, Service Bus, and IOE rule engines.
• - Design reusable, modular components for feature engineering, experimentation, and inference.
• - Build automated training, retraining, and evaluation pipelines in Databricks / Azure ML / Python.
• - Develop CI/CD workflows for model deployment using Azure DevOps / GitHub Actions.
• - Create scalable inference endpoints using Azure Functions, Container Apps, or APIM.
• - Implement monitoring for model drift, data quality, and performance degradation.
• - Work closely with Data Engineering to design robust data pipelines.
• - Build feature stores, feature pipelines, and data transformations optimized for machine learning.
• - Ensure traceability, reproducibility, and well documented data assets.
• - Integrate ML outputs into real-time orchestration flows.
• - Contribute to the IOE decisionctree, scoring logic, and step orchestration.
• - Build ML-driven triggers for automated campaigns, safety alerts, and proactive outreach.
• - Work with the Director of Data Science to define the ML roadmap for IOE.
• - Partner with Product for personalization, engagement, and predictive feature development.
• - Coordinate with Engineering to deliver API endpoints, event triggers, and user facing functionality.
• - Contribute to documentation, experimentation logs, governance, and compliance.

Machine Learning Engineer

ERP Analytics & AI Architect Lead

Director, Data Infrastructure – MLOps/LLMOps

Machine Learning Engineer

Staff ML Engineer – Dynamic World Perception