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AI Engineer with less than a year in NLP & Data Annotation
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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.
Vaagdevi College Of Engineering
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
India
Exceed Management
Data Annotator
January 1, 2024 – July 1, 2024
India
AI Skills Passport Generative AI
EY & Microsoft
June 1, 2026 – Present
Data Annotation Specialist Certification
Unknown
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 Generative AI and Data Annotation demonstrate a proactive approach to skill development and a strong interest in the AI domain. However, the experience is entry-level, and the breadth of exposure to diverse AI projects or advanced engineering challenges is limited, which might require significant ramp-up for a senior AI Engineer role.
Soft Skills & Operational Fit
The candidate's resume highlights collaboration with data teams and contributions to improving dataset quality, suggesting an ability to work in a team and focus on quality. The experience as a Process Associate and Data Annotator indicates attention to detail and adherence to guidelines, which are crucial for operational roles in AI data pipelines.