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
