ML Ops EngineerLocation : BengaluruExperience : 4 - 6 YearsOpen Positions : 2Shift Timings : 1 : 30 PM - 10 : 30 PM ISTEducation Qualification : B.TechMandatory Skills : - Machine Learning- Python- Data Warehousing- AWS- Data Analytics- GrafanaExperience Requirements : - Overall experience of 4 - 6 years, with at least 2 - 3 years of experience in MLOps, ML/Data Engineering, or SRE for data and ML systems.- Experience collaborating with globally distributed ML Engineering, Model Services, and Tech Ops/SRE teams.- Flexibility to work with regular overlap with US Pacific Time business hours.Roles & Responsibilities : - Monitor model quality in production, including prediction distributions, feature drift, model performance, and data freshness.- Own dashboards, alerts, and incident triage using Grafana, Datadog, and Prometheus, ensuring documented runbooks for every alert.- Lead model incident triage across infrastructure, models, and data, and drive Root Cause Analysis (RCA) activities to closure.- Contribute to Data Quality Monitoring frameworks and maintain training and inference pipelines using Databricks/Spark.- Support platform migrations, including Lakehouse, Unity Catalog,



and runtime upgrades, with validation before and after each migration.- Improve CI/CD processes and code quality for ML jobs.- Support model teams through technical documentation, knowledge transfer, and onboarding sessions.Project Overview : - The role involves supporting production Machine Learning systems powering personalization, recommendation engines, and search-ranking solutions.- Models are trained and scored through Databricks/Spark-based batch pipelines. Features are published to feature stores and served through model-serving infrastructure.- The primary responsibility of this role is to ensure that models continue to perform as expected in production environments, beyond infrastructure availability and system uptime.- The engineer will focus on : 1. Production model quality monitoring.2. Model and feature drift detection.3. Data pipeline health and freshness monitoring.4. Model-specific dashboards and alerting.5. Feedback loops and continuous improvement of ML operations. (ref:hirist.tech)

📌 BDB - MLOps Engineer (Bengaluru)
🏢 BizViz Technologies
📍 Bengaluru