Aftaab Jariwala

About Aftaab Jariwala

I build production-grade ML systems that deliver measurable results — from LSTM-based fraud detection pipelines with 98% accuracy to LLM-powered RAG applications that cut data retrieval latency by 20%. With 4+ years of experience across NLP, computer vision, and generative AI, I specialize in the full ML lifecycle: from research and model development to cloud deployment at scale.My technical toolkit spans deep learning architectures (BERT, GPT, CNN, RNN, Transformers), Python-based ML frameworks (PyTorch, TensorFlow, Scikit-learn), and cloud platforms (AWS SageMaker, EC2, Lambda, GCP Vertex AI). I\'ve built and shipped inference backends using FastAPI, managed data pipelines across SQL, PostgreSQL, MongoDB, and Neo4j, and leveraged tools like Tableau and Power BI to translate model outputs into business insights.I hold an MS in Computer Science from Western New England University and bring a strong foundation in statistical modeling, hypothesis testing, and A/B testing — ensuring my solutions are both technically sound and business-aligned.Currently open to full-time ML Engineer roles in the US. Let\'s connect.

Experience

1. Data Scientist

   Alpha Recon Technologies

   Sep 2025 — Present

   Engineered and deployed end-to-end ML pipelines for threat and risk intelligence, processing large multi-source datasets to surface actionable security insights with measurable improvements in detection speed.• Designed and fine-tuned machine learning models (NLP, classification, anomaly detection) optimized for accuracy, scalability, and production readiness across security intelligence use cases.• Built robust, automated data pipelines for ingestion, preprocessing, model training, and inference — reducing manual data handling time by ~30% and enabling continuous model retraining.• Developed proof-of-concept (PoC) ML solutions using Python, PyTorch, and Scikit-learn to rapidly validate new approaches, accelerating the path from research to production deployment.• Collaborated with cross-functional security and engineering teams to translate threat detection requirements into deployable ML solutions integrated with existing intelligence platforms.• Implemented model monitoring using defined performance metrics, identifying and resolving model drift issues to maintain reliability and effectiveness in production environments.• Maintained data quality and integrity across the full data lifecycle; authored comprehensive documentation for models, pipelines, and methodologies to ensure reproducibility and team knowledge transfer.

Education

• Western New England University

  Master of Science, Computer Science

• Vellore Institute of Technology

  Bachelor's degree, Electrical and Electronics Engineering

  2017 — 2021