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AI Engineer with less than a year in NLP pipelines & data annotation.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Detail-oriented aspiring AI Engineer with experience in NLP pipelines, data annotation, and dataset preparation. Proficient in Python, Pandas, Scikit-learn, and familiar with ETL pipelines and data engineering workflows. Interested in building scalable AI solutions across NLP and data-driven systems.
Masters in Computer Applications (MCA)
Masters in Computer Applications (MCA)
August 1, 2024 – June 30, 2026
Chaitanya Deemed to be University
Bachelor of Science · Mathematics, Physics, Computer Science
August 1, 2020 – June 30, 2023
SR Junior College
Intermediate
June 1, 2018 – May 31, 2019
JSM High School
Secondary Education
June 1, 2017 – May 31, 2017
HanDigital Solutions (P) Ltd
Process Associate
August 1, 2024 – September 1, 2024
Delhi, Delhi, India
Exceed Management
Data Annotator
January 1, 2024 – July 1, 2024
Delhi, Delhi, India
Data Annotation Specialist Certification
Unknown
June 1, 2026 – Present
AI Skills Passport
EY & Microsoft
June 1, 2026 – Present
SQL Certification
Coursera
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's experience is primarily in data annotation and preprocessing, which aligns with foundational aspects of AI engineering. The pursuit of an MCA and certifications in AI and SQL indicate a proactive approach to skill development. The roles held are entry-level, suggesting a fit for a team that values growth and hands-on data work. The breadth of skills listed (multiple programming languages, AI/ML libraries, core concepts) shows a diverse interest, but the practical application has been focused on data preparation.
Soft Skills & Operational Fit
The candidate's experience as a Process Associate and Data Annotator suggests an ability to follow guidelines, perform detailed tasks, and collaborate with data teams. The descriptions imply a focus on data integrity and quality, which are crucial for operational fit in AI/ML pipelines. However, there is insufficient data to assess stress handling or team collaboration beyond basic task execution.