Hi, I'm Alankrit.
Building explainable, scalable systems that power smarter decisions.
About Me
An AI/ML Engineer passionate about building explainable, scalable systems that power smarter decisions across finance and technology.
I specialize in designing intelligent AI models, scalable data pipelines, and insight-driven analytics that bridge complexity with clarity. My strength lies in transforming challenging data problems into transparent, impactful, and future-ready solutions.
Featured Projects
Stock RAG • SHAP • DeepSeek
Explainable Portfolio Insights
- Built RAG system combining SHAP explainability with XGBoost for transparent portfolio analysis
- Integrated DeepSeek LLM for natural language explanations of model predictions
- Reduced insight generation time by 60% while maintaining explainability standards
Finance Sentiment Analysis
Forecasting Market Regimes
- Developed real-time news ingestion pipeline processing 10K+ financial articles daily
- Implemented regime shift detection using sentiment signals and market volatility patterns
- Created early warning system for risk signals with 85% accuracy in regime prediction
- Aggregated, cleansed and combined data from Formula One data source to create a Tableau dashboard
- Analyzed high performance athlete data over 18 years (2003-2021)
- Applied data wrangling, time series analysis, and statistical analysis for performance insights
Technical Skills
Languages
ML & AI
LLM & Retrieval
Model Validation & Risk
MLOps & Cloud
Data Systems
Professional Experience
Data Engineer Intern
Verity Advisor LLC
Denton, TX
- Led the design and delivery of real-time anomaly detection and regime classification systems across 1M+ daily financial transactions
- Owned end-to-end development of drift-aware time-series models with out-of-distribution validation, improving early risk detection precision by 18%
- Built automated evaluation, retraining, and performance regression pipelines, enabling daily production refresh with fully reproducible training
- Guided non-technical stakeholders through model interpretation and anomaly diagnostics, reducing manual investigation time by 12+ hours per week
Graduate Teaching Assistant
University of North Texas
Denton, TX
- Built Python-based validation, anomaly detection, and clustering templates used across 300+ student ML submissions, reducing evaluation workload by 40%
- Designed reproducible ML workflow scaffolds for dataset validation, mislabeled sample detection, and model diagnostics
- Mentored 30+ advanced students on error analysis, feature leakage, and model interpretability
DevOps Engineer
Pitney Bowes
New Delhi, India
- Engineered distributed Java/Python platforms processing 10M+ operational events/day, supporting real-time ML inference availability
- Built streaming ingestion and anomaly monitoring pipelines that reduced data-quality failures by 33% across logistics workflows
- Implemented microservices and optimized feature pipelines, improving ML inference feature availability by 30%
- Automated CI/CD pipelines (Docker, Jenkins), reducing batch processing cycles by 25% and operational failures by 20%
Education
Master of Science, Advanced Data Analytics
University of North Texas
Denton, TX
- Teaching assistant to graduate and undergraduate students for Statistics, Machine learning, and Data Visualization coursework
- Mentored 90+ students in SQL, forecasting, and reporting workflows, cutting grading turnaround by 40%
- Relevant Coursework: Statistics, Quantitative Methods, Forecasting
Awards & Recognition
Certificate of Achievement
Verity Advisors LLC
2025
Recognized for delivering a cloud-native ETL & analytics pipeline — designed scalable ETL workflows, optimized data pipelines, and integrated analytics to drive business decisions.
Outstanding Performance Award
Pitney Bowes
2023
Recognized for exceptional contributions to fraud detection system improvements
Innovation in ML Applications
Internal Recognition
2022
Led development of explainable AI framework for financial risk assessment
Get In Touch
I'm always interested in discussing new opportunities, collaborating on exciting projects, or just having a conversation about AI and data science. Feel free to reach out!