Navikara

Machine Learning Engineer - Data Quality

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Last date

26/06/2026

Openings

1 or more

Qualification

Snowflake

Post

Full Time

Overview

Job descriptionAt Apple, we rely on high-quality data to drive critical decisions across our global operations. We are looking for a Machine Learning Engineer with software engineering skills to develop and deploy ML-driven data validation solutions that ensure data integrity. In this ro...

Company
Powered by Monster
Location
Ganganagar; Rajasthan; Uttar Pradesh
Salary
500000 - 1000000 (per annum)
Skill Required
Snowflake
Duration
Not specified
Posted On
28/4/2026
Company Client
(On behalf of Applexus Technologies)
External Apply URL
True

Detail Fields

Sector: Other Service Activities Functional Area: Human Resources Functional Role: Fresher Total Experience In Years: 3 - 10 Nature Of Job: Full Time Gender Preferences: Any Ex Servicemen Preferred: No

Eligibility

Criteria Education: Job Requirement as per Employer posting Label Criteria Skill: Job Requirement as per Employer posting Criteria Experience: Job Requirement as per Employer posting Criteria Location: Job Requirement as per Employer posting Yes: No

Full Detail Text

Company Name: Powered by Monster (On behalf of Applexus Technologies) Click here for more details Job Title Machine Learning Engineer - Data Quality Organisation Type Sector Other Service Activities Functional Area Human Resources Functional Role Fresher Job Description Job descriptionAt Apple, we rely on high-quality data to drive critical decisions across our global operations. We are looking for a Machine Learning Engineer with software engineering skills to develop and deploy ML-driven data validation solutions that ensure data integrity. In this role, you will build scalable anomaly detection systems, work on Gen AI projects, collaborate with data engineering teams to enhance data quality frameworks, and drive innovations in MLOps and data monitoring.- Develop ML-based data validation and monitoring solutions, focusing on anomaly detection and explainability.- Analyze large datasets to detect data drift, integrity issues, and emerging quality risks.- Apply the full ML lifecycle, from exploratory data analysis (EDA) and feature engineering to model selection, training, deployment, and monitoring.- Experiment with different methodologies to improve model accur