ML Ops Engineer
First seen by Alion on Nov 4, 2026.
ML Ops Engineer
Location: Bengaluru
Experience: 4 - 6 Years
Open Positions: 2
Employment Type: Contract
Project Duration: 6 Months
Shift Timings: 1:30 PM - 10:30 PM IST
Education Qualification: B.Tech
Mandatory Skills:
- Machine Learning
- Python
- Data Warehousing
- AWS
- Data Analytics
- Grafana
Experience 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.
Skills
MLOps, DevOps, Machine Learning, Python, AWS, Data Warehousing, Data Analytics, Grafana, Databricks, Hadoop, CI/CD