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Looking for new opportunity | Ex-HumanizeIQ
Graduated from IIIT Kottayam with a Bachelor of Technology in Computer Science, specializing in developing robust data engineering solutions. Currently working as a Data Engineer at HumanizeIQ, where expertise in building and managing end-to-end ELT pipelines with tools like Cloudflare D1, R2, Trino, and Metabase supports scalable analytics and self-serve BI. Proficient in FastAPI, Docker Swarm, and Apache Superset, with demonstrated ability to streamline data operations through workflow automation using n8n. At HumanizeIQ, contributed to enhancing data reliability and reporting efficiency by implementing automated processes with the team. Passionate about leveraging technical skills to empower organizations with data-driven insights and solutions. Aims to foster collaboration and innovation while bringing technical acumen and fresh perspectives to solve complex challenges.
IIIT Kottayam
Bachelor of Technology - BTech, Computer Science
December 1, 2020 – April 1, 2024
HumanizeIQ
Data Engineer
November 1, 2025 – Present
Remote
HumanizeIQ
AI Intern
June 1, 2025 – October 1, 2025
Remote
Unified Mentor
Project Intern
March 1, 2025 – April 1, 2025
Remote
Generative AI with Large Language Models
DeepLearning.AI, Amazon Web Services
June 23, 2026 – Present
Generative AI with Large Language Models
EDX Alumni
June 23, 2026 – Present
Cultural Fit Analysis
The candidate's experience is primarily in Data Engineering and AI Intern roles, with a strong focus on data infrastructure and some exposure to AI. While the certifications align with an ML Engineer role, the practical experience is more geared towards data engineering. The limited project diversity and the short duration of some internships suggest a developing career path. The target role of ML Engineer requires a deeper foundation in machine learning algorithms, model deployment, and MLOps, which is not extensively demonstrated in the current experience. This indicates a moderate cultural fit, with a need for further development in core ML engineering areas.
Soft Skills & Operational Fit
The candidate's experience descriptions suggest an ability to work with data pipelines and automation, which implies problem-solving and operational efficiency. However, without specific psychometric or English test results, a detailed assessment of soft skills and operational fit is not possible.