• End-to-End AI Delivery: Hands-on experience building, optimizing, and scaling machine learning systems from concept to production—prioritizing clean code, fast iteration, and robust deployment, backed by over 3.5 years of professional experience.
• Explainable & Trustworthy Solutions: Specialize in Explainable AI (XAI) and model transparency, making your models not just accurate but understandable and actionable for stakeholders.
• Innovation for Business Impact: Translate cutting-edge research into real business value, leveraging deep expertise in deep learning, computer vision, reinforcement learning, and MLOps.

M.Tech in DS-ML-AI (CGPA 9.8/10) • Certified in Deep Learning & Machine Learning (Stanford, DeepLearning.AI)

My mission: Making advanced AI solutions explainable, scalable, and accessible for real-world applications.

Research & Publications

Data Science, Machine Learning & AI

• Explainable AI Framework - Paper
  

  Details hidden as paper undergoing double-blind review.

• Explainable AI - Survey Paper

  Under Review.

• SmartFlow: RL & Agentic AI for Bike-Sharing
  Ongoing work (private repo).

  Hybrid system using deep reinforcement learning for station-level bike redistribution and agentic AI to autonomously dispatch staff—reducing idle time and operational costs.

Mechanical Engineering

• Corrosion Behaviour of Al 6061/MWCNT Composite
  Int'l Conf. on Advances in Materials Processing & Manufacturing Applications (Springer LNM), 23 Jun 2021.

  Produced Al 6061 with 0–1.5 wt% CNT via double-stir casting; 1 wt% CNT reduced corrosion rate by ~X% in 3.5% NaCl environments (SEM-verified).

  🔗 DOI: 10.1007/978-981-16-0909-1_29

• Thermal & Mechanical Characterization of OMMT-Treated ABS
  Materials Today: Proceedings 46(6), Oct 2020.

  Coated FDM-printed ABS with 2–6 wt% OMMT nanoclay via acetone-deposition; 4 wt%/70 s treatment boosted tensile strength to 38.9 MPa, flexural to 48.6 MPa, and raised Tg by 5 °C.

  🔗 DOI: 10.1016/j.matpr.2020.09.676

Experience

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Education

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Projects

Professional Scale (At an organisation)

• Payments Modernisation -- [Large UK-based Financial Services Organisation]

Enhanced digital payments by developing a cloud-native platform designed for secure and fast transactions. Prioritised resilience and compliance to meet regulatory demands, ensuring the platform could scale effectively amid the growing digital payments landscape. The solution was built to support robust transaction processing, providing a secure environment while maintaining compliance with industry standards and regulations, ultimately improving overall system reliability and performance in a dynamic market.

• Fraud Data Analytics -- [Large US-based Financial Services Organisation]

Enhanced the connectivity across the data ecosystem to enable seamless, secure, and swift data operations, ensuring smooth integration between various components of the architecture. Developed an elegant solution that efficiently linked Kafka streams with Python-based data ingestion, optimising the process for real-time data processing. This solution improved the speed and efficiency of data ingestion while bolstering the system's dependability by reducing latency, ensuring reliable data flow, and maintaining high data integrity across the pipeline.

• AIMS, Hermes & CN Shipping Squad -- [Large UK-based Energy Organisation]

Enhanced operations by developing and maintaining three core applications: AIMS, Hermes, and Casualty Notifier. AIMS includes the Advisor Portal for terminal assessments and vessel inspection tracking, and the Requestor Portal for raising inspection requests. Hermes serves as the source of truth for data on locations, organisations, vessels, cargo, and deals. Casualty Notifier ensures prompt communication of casualties during vessel journeys, notifying relevant parties in real-time. These applications streamline processes, improve data access, and enhance operational efficiency.

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Personal Interest

Skills

• Languages:
  • Programming Languages: Python and Java
  • Database Query Languages: SQL, Cypher QL
  • Web Technologies: HTML, CSS, JavaScript, Bootstrap and Jinja
• Machine Learning & Deep Learning Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost, LightGBM, CatBoost
• DS-ML-AI Concepts:
  • Data Preprocessing: Data Wrangling, Outlier Handling, Winsorization, Data Imputation, Missing Value Handling, Feature Engineering, Feature Selection, Transformations, Scaling, Normalisation, Train-Test Split, One-Hot Encoding, Label Encoding
  • EDA: Univariate Analysis, Multivariate Analysis, Data Visualisation (Seaborn, Matplotlib), Correlation Analysis, Error Analysis
  • Statistical Methods: Hypothesis Testing, Chi-Square Test, ANOVA, Tukey HSD, OLS, PCA, SVD, Bayesian Statistics, CLT, Statistical Significance
  • Encoding Techniques: Ordinal, One-Hot, Dummy Variables, Target, Frequency Encoding
  • Regression: Linear, Ridge, Lasso, Polynomial, ElasticNet, Random Forest, Gradient Boosting, XGBoost, CatBoost, LightGBM, SGD
  • Classification: Logistic Regression, KNN, SVM, Decision Trees, Naive Bayes, Random Forest, XGBoost, LightGBM, Neural Networks, Ensemble, Bagging, Boosting
  • Clustering: K-Means, Hierarchical, DBSCAN, GMM, Agglomerative, Mean Shift, Spectral Clustering
  • Neural Networks: Feedforward, CNN, RNN, LSTM, Autoencoders, GANs, Transfer Learning, Backpropagation, Dropout, Batch Norm, Activation Functions
  • Dimensionality Reduction: PCA, t-SNE, UMAP, LDA, ICA
  • Model Evaluation & Tuning: Cross-Validation (K-Fold, Stratified), Grid/Random Search, Hyperparameter Tuning, Metrics (Accuracy, Precision, Recall, F1, AUC-ROC, Confusion Matrix), Overfitting, Bias-Variance Trade-off
  • Advanced Concepts: Reinforcement Learning, Deep RL, NLP, Computer Vision, SHAP, LIME, Bayesian Optimisation
• Cloud: Amazon Web Services (AWS)
• Big Data & Distributed Computing: Apache Spark, Hadoop
• APIs: RESTful API Development (Flask, FastAPI, Spring Boot), API Testing (Postman)
• Security: OAuth 2.0, JWT, SSL/TLS
• MLOps & CI/CD: MLflow, DVC, Jenkins, GitHub Actions, Docker, Kubernetes
• Frameworks:
  • Backend: Flask, FastAPI, Spring Boot
  • Web Scraping & Automation: Beautiful Soup, Selenium
• Libraries:
  • Data Science: Pandas
  • Statistics: StatsModels, Scipy.stats
  • Mathematics: NumPy, SciPy, SymPy
  • Data Visualisation: Matplotlib, Seaborn, Plotly
  • NLP: NLTK, spaCy, Hugging Face Transformers
  • Computer Vision: OpenCV, PIL
  • Audio Processing: SoundDevice, SoundFile
  • UI Development: Tkinter
  • Regex: Regex
• Database: MongoDB, Neo4J, PostgreSQL, MySQL
• Messaging Queue: Apache Kafka, Apache Active MQ
• Testing Frameworks: PyTest, JUnit, Mockito, Cucumber
• Other Tools: SonarQube, Artifactory, Harness, ElasticSearch, Synk, Fortify on Demand, AppDynamics, Twistlock, Jira, Confluence
• Visualisation Tools: Tableau, Microsoft Power BI
• Version Control: Git, GitHub, BitBucket, Azure DevOps
• IDEs / Editors: PyCharm, Anaconda, VSCode, Jupyter Notebook, Google Colaboratory, Kaggle, Spyder, IntelliJ IDEA, Notepad ++
• Operating Systems: Linux, Windows

Awards

Infosys

• Gracias Award for excellent performance in September 2024.
• Gracias Award for outstanding contributions in meeting critical project deadlines - February 2025.

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Ernst & Young

• User Appreciate Award for contributing to team success in March 2024.

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Accenture

• Recognised with a Client Award from the COO and CIO of the organization in April 2023.
• Acknowledged as the Top Performer, receiving awards in Dec 2022, Feb, and May 2023.
• Client & Accenture Leadership Appreciation – Recognised for technical expertise and impactful contributions, May 2023.
• Honoured with the prestigious ACE Award, a pinnacle achievement at Accenture, in June 2023.
• Contributed to team success, receiving a Team Milestone Award in December 2023.