Open to opportunities

ShafeekAnsari User

@shafeekansariuser

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AI Data Engineer with expertise in scalable AI platforms.

Zimbabwe

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What I'm looking for

I am looking for opportunities that foster innovation, collaboration, and growth in AI and data engineering.

As an AI Data Engineer, I specialize in building scalable AI platforms and maintaining high-fidelity data systems. My experience includes deploying intelligent solutions using advanced language models such as GPT and BERT. I have a proven track record of improving data integrity and optimizing resource usage through robust pipelines and anomaly detection models.

During my internship at Codem Technologies, I constructed a modular AI system that improved risk prediction precision by 29%. I also developed Python-based automation for quality checks, significantly reducing manual validation efforts. My collaborative work with senior engineers led to the deployment of fault-tolerant pipelines, enhancing performance by 32%.

With a strong educational background in Artificial Intelligence and Data Science, I have honed my skills in machine learning, data modeling, and cloud-based deployments. I am passionate about leveraging my expertise to create innovative solutions that drive data-driven decision-making.

Experience

Work history, roles, and key accomplishments

AI/Data Engineering Intern

Codem Technologies

Feb 2025 - Mar 2025 (1 month)

Constructed a modular AI system leveraging BERT and GPT models for risk prediction, improving precision by 29%. Developed Python-based automation for quality checks across derived datasets, reducing manual validation by 45%. Integrated Oracle-based metrics monitoring with anomaly detection logic, flagging 17.

BERT GPT Python Oracle Spring AWS Lambda Data Pipelines Anomaly Detection Fault Tolerance Performance Tuning

AI ML Intern

Ineuron.ai

Apr 2022 - May 2022 (1 month)

Engineered machine learning models to score data reliability across finance use cases using XGBoost and pandas. Tuned custom neural nets using TensorFlow for anomaly detection in transaction-level feeds. Improved F1-score from 76% to 89% on outlier detection through iterative data transformations and hyperparameter tuning.