• 📊 Data Validation & Monitoring: Continuously monitor and validate datasets used in the development and deployment of AI/ML models.
• 🧹 Data Profiling & Cleansing: Conduct data profiling, cleansing, enrichment, and transformation activities to maintain high-quality datasets.
• 📈 KPI & Dashboard Management: Develop and manage data quality metrics, KPIs, and dashboards to track quality improvements.
• 🧩 Root Cause Analysis: Investigate data anomalies, identify root causes, and work with stakeholders to implement corrective actions.
• 🔄 Collaboration with Data Engineers: Define and implement data validation rules and support the automation of data quality pipelines.
• 📜 Governance & Compliance: Ensure compliance with organizational data governance, security, and privacy policies.
• 🏷️ Dataset Curation: Assist in the curation and quality control of labeled datasets used for supervised learning and model training.
• ✅ Annotation QA: Review and validate annotated data for training, validation, and test datasets to ensure labeling accuracy.
• 🧭 Guidelines & Tool Feedback: Provide actionable feedback to improve data labeling guidelines and enhance annotation tools.
• 🤖 Model Evaluation Participation: Support model evaluation processes to verify data consistency and alignment with AI model standards.

• Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field.
• 2–5 years of experience in a data quality, data analyst, or AI/ML support role.
• Strong understanding of data structures, data pipelines, and data wrangling techniques.
• Hands-on experience with SQL, Python (Pandas, NumPy), and data profiling tools.
• Familiarity with data annotation platforms and labeling tools.
• Knowledge of AI/ML concepts and lifecycle is preferred.
• Excellent analytical thinking and problem-solving skills.
• Strong communication and documentation abilities.

• Experience with cloud data platforms (e.g., AWS, Azure, GCP).
• Familiarity with MLops pipelines and model evaluation frameworks.
• Understanding of GDPR and data privacy compliance in AI systems.

Education Level : Bachelor's Degree

Technical Education : N/A

Certificate : TOT Certified in AI - DevOps Engineer

Industrial Certificate : Experience

Key Skills
Attention to Detail, Technical Skills, AI/ML-Specific Skills, Time Management.

Other Skills
Problem Solving

• English - Reading, Writing, Speaking
• Hindi - Reading, Writing, Speaking