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Poornima Jayadevan

@poornimajayadevan

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Machine learning and Explainable AI engineer building interpretable, deployable classification systems.

Germany

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What I'm looking for

I’m looking for full-time work in applied machine learning with strong focus on explainability. I want to build interpretable, production-ready systems that combine ML with reliable APIs, scalable deployment, and evaluation grounded in real data.

I’m a final-semester Master’s student in Artificial Intelligence at BTU Cottbus-Senftenberg, focused on Explainable AI (XAI) for classification models. I build machine learning systems that are interpretable, reliable, and scalable—so stakeholders can trust both predictions and explanations.

In my Risk and Regulatory Working Student role at PwC Deutschland, I developed end-to-end NLP-based text classification models using Transformer architectures (BERT, RoBERTa) and traditional algorithms (Random Forest, Naive Bayes). I paired strong data engineering and evaluation (Accuracy, Precision, Recall, F1-score) with anomaly detection (Isolation Forest) to surface actionable insights from large regulatory datasets.

Previously as an Assistant Systems Engineer at Tata Consultancy Services, I designed and delivered Python-based APIs and backend components in a SOA environment, tested with Postman, and supported via CI/CD and incident management. I also bring a problem-first mindset from ethical hacking and mobile development internships—now applied to delivering production-ready, explainable AI solutions.

Experience

Work history, roles, and key accomplishments

Risk and Regulatory Working Student

PwC Deutschland

Jan 2025 - Oct 2025 (9 months)

Developed end-to-end NLP text classification pipelines using Transformer models (BERT, RoBERTa) and traditional ML (Random Forest, Naive Bayes), including preprocessing and feature engineering (TF-IDF, Word2Vec). Applied model evaluation metrics (accuracy, precision, recall, F1) and used Isolation Forest for anomaly detection in regulatory datasets, reporting insights in Excel and PowerPoint.

BERT RoBERTa TF IDF Word2Vec Random Forest Naïve Bayes Hyperparameter Tuning Cross Validation Isolation Forest

Assistant Systems Engineer

Tata Consultancy Services

Jul 2019 - Jan 2021 (1 year 6 months)

Contributed to banking and insurance systems by designing and implementing Python-based APIs and backend components within a SOA environment. Tested and validated APIs with Postman, managed code with Git/Bitbucket and CI/CD workflows using Pytest, and supported production issue resolution through Jira and Agile delivery.

Python REST APIs IBM Websphere Postman Pytest Git Bitbucket Oracle SQL CI CD Agile

Ethical Hacking Intern

All India Council for Robotics & Automation

May 2018 - Aug 2018 (3 months)

Performed ethical hacking and penetration testing using Python with Nmap and Metasploit to identify and exploit network and system vulnerabilities. Conducted traffic analysis and deep packet inspection with Scapy, executed vulnerability assessments with controlled exploitation, and recommended mitigations aligned with cybersecurity best practices.

Ethical Hacking Penetration Testing Nmap Metasploit Python Scapy Traffic Analysis Deep Packet Inspection Vulnerability Assessment Risk Mitigation

Mobile Application Development Intern

Aabasoft

Apr 2017 - Jun 2017 (2 months)

Developed Android applications in Java using modular components, proper activity lifecycle management, and reusable UI elements to improve scalability and maintainability. Built responsive interfaces with Android Studio/XML following Material Design, and supported feature development, debugging, and functional/UI testing.

Java Android Development Android Studio XML Layouts Material Design Activity Lifecycle UI Testing Functional Testing Responsive UI

Education

Degrees, certifications, and relevant coursework

Brandenburg Technical University (BTU Cottbus-Senftenberg)

M.Sc. in Artificial Intelligence, Artificial Intelligence

2022 -

Grade: 1.8 (German grading system)

Final-semester M.Sc. student in Artificial Intelligence (GPA: 1.8, German grading system) with an ongoing thesis focused on Explainable AI (XAI) for classification tasks.