Hi there! I'm Mohammed Siddiq. Technology professional with a B.Tech in AI/ML and hands-on experience supporting enterprise-grade systems for a US-based client.
My core strengths span Python automation, SQL-based reporting, system integration, and compliance documentation — built through daily operational work at Tech Mahindra, not just coursework. I have a strong foundation in machine learning, NLP, and MLOps tooling, with a track record of shipping working solutions independently.
I'm seeking to grow in an environment where technology directly serves a meaningful mission.
Achievements
- 850+ GitHub contributions across open-source projects — consistent, documented engineering habit
- Published a VS Code theme with 50+ downloads — shipped a real product end-to-end independently
- Personal portfolio website built from scratch — 50% measurable increase in professional outreach
Experience
Systems & Technical Operations Engineer, Tech Mahindra | US-based Enterprise Client Engagement
Aug 2025 – Present
- Application Support & Incident Management: Handle day-to-day support for networked enterprise systems — diagnosing production failures, isolating root causes, and implementing fixes while minimising downtime. Manage full incident lifecycle from triage to resolution documentation.
- SQL & Data Integrity: Use SQL to query operational datasets, generate performance reports, and support data integrity checks when discrepancies are flagged across integrated system components.
- Integration Validation: Validate end-to-end communication across networked components — verifying event-triggered workflows fire correctly and tracing failures when they don't. Work closely mirrors integration testing in software infrastructure.
- Compliance Documentation: Maintain audit-ready documentation covering configurations, change logs, and resolution records aligned with client data security and compliance requirements.
- GitHub Collaboration: Manage version-controlled operational documentation and config files via GitHub — feature branches for isolated testing, PRs for team review before rollout.
Machine Learning Intern, Ignitus
February 2024 – May 2024
- Implemented supervised learning algorithms (Logistic Regression, Decision Trees, Naive Bayes) and NLP pipelines (TF-IDF, tokenization, text classification) to add intelligent features to a production LMS, contributing to a 30% improvement in user engagement.
- Integrated Hugging Face transformer models for content personalisation; collaborated via GitHub across a fully remote team — managing feature branches, raising PRs, and participating in peer code reviews throughout the project lifecycle.
Web Developer (Freelance), Excel Placement Services
Jan 2024 – Present
- Built and deployed a full-stack responsive website end-to-end — requirements gathering, UI development, hosting, and DNS setup — resulting in a 40% increase in client acquisition inquiries.
- Translated non-technical stakeholder requirements into functional specifications, delivering iteratively based on feedback across the engagement lifecycle.
Technical Skills
Languages & Scripting
Python, SQL
AI / ML
Supervised & Unsupervised Learning, NLP, Deep Learning, Generative AI, LLMs, Prompt Engineering, Attention Mechanisms, Transfer Learning
Libraries & Frameworks
Scikit-Learn, TensorFlow, Keras, Transformers (Hugging Face), NLTK, Pandas, NumPy, Matplotlib, Flask
MLOps & DevOps
MLflow, DVC, Docker, GitHub Actions, CI/CD pipelines
Tools & Platforms
Git/GitHub, Hugging Face Hub, Salesforce, VS Code
Projects
Natural Language Question Answering System
Built a Flask-based application enabling users to query PDF documents using transformer-based NLP models. Designed the REST API layer, managed backend integration, and documented the full system — demonstrating end-to-end ownership from model selection to deployment.
View ProjectHouse Price Prediction System
Developed an end-to-end ML pipeline covering data ingestion, feature engineering, model training (Linear Regression, Random Forest), hyperparameter tuning, and a web interface for real-time predictions — focused on interpretable output for non-technical users.
View Project