Available to hire
AI/ML Engineer with 4+ years of experience in Machine Learning, Deep Learning, Generative AI, and Retrieval-Augmented Generation (RAG), building scalable production-grade solutions. Proficient in Python, PyTorch, TensorFlow, and modern AI/ML tooling to deliver measurable improvements across fraud prevention, predictive accuracy, risk intelligence, and enterprise automation.
Experienced in end-to-end GenAI/MLOps: designing vector search & hybrid retrieval systems, building citation-grounded RAG workflows, and deploying models on AWS EKS/Kubernetes with robust governance (RBAC, audit logging, PII detection). Delivered real-time analytics and NLP/LLM applications for banking and fintech use cases with strong compliance and operational efficiency focus.
Skills
Experience Level
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
Intermediate
Intermediate
Language
Work Experience
AI/ML Engineer at Goldman Sachs Group
August 1, 2025 - PresentArchitected an enterprise GenAI RAG platform using GPT-4, Llama 3, LangChain, Pinecone, and Bloomberg datasets, reducing financial research time by 68% across 15M+ documents. Engineered hybrid semantic retrieval pipelines with Pinecone, Elasticsearch vector search, FAISS, and query re-ranking, improving retrieval accuracy from 74% to 93%. Developed financial NLP models (PyTorch, Hugging Face, spaCy) for entity extraction and risk intelligence generation over 15M+ records. Implemented citation-grounded RAG workflows using OpenAI APIs, LlamaIndex, LangGraph, and contextual prompting, reducing hallucination rate by 40% during validation. Optimized inference pipelines on AWS EKS/Kubernetes, Docker, MLflow, and Databricks, reducing response latency by 45%. Governed AI compliance using guardrails, moderation, RBAC, vault/audit logging, and PII detection; reduced compliance review turnaround by 60%. Orchestrated end-to-end GenAI ops pipelines using Airflow, Kafka, Terraform, GitHub Actions, J
AI/ML Engineer at Goldman Sachs Group, USA
August 1, 2025 - PresentArchitected an enterprise GenAI RAG platform using GPT-4, Llama 3, LangChain, Pinecone, and Bloomberg datasets, reducing financial research time by 68% across 15M+ documents. Engineered hybrid semantic retrieval pipelines with Pinecone, Elasticsearch vector search, FAISS, and query re-ranking, improving retrieval accuracy from 74% to 93%. Developed finance NLP models using PyTorch, Hugging Face, spaCy, and SEC EDGAR data to automate entity extraction and risk intelligence across 15M+ records. Implemented citation-grounded RAG workflows using OpenAI GPT-4, LlamaIndex, LangGraph, and contextual prompting, reducing hallucination rate by 40% during validation. Optimized enterprise-scale GenAI inference pipelines on AWS EKS, Kubernetes, Docker, MLflow, and Databricks, improving response latency by 45% for global banking users. Governed AI compliance with Guardrails AI, OpenAI moderation, RBAC, Vault, audit logging, and PII detection, reducing compliance review turnaround time by 60%. Orches
Machine Learning Engineer at Mphasis
January 1, 2021 - April 1, 2023Built credit-risk models using XGBoost, LightGBM, and CatBoost with PySpark and AWS EMR, achieving 91% predictive accuracy and reducing defaults by 24%. Designed Spark-based feature engineering pipelines generating 500+ predictive attributes from UPI, GSTN, Account Aggregator, and banking datasets; increased underwriting automation by 82%. Optimized loan decisioning workflows using Logistic Regression, PCA, Optuna tuning, and MLflow, reducing approval turnaround from 48 hours to under 5 minutes. Developed real-time fraud detection models using XGBoost, Random Forest, Isolation Forest, and SHAP explainability; achieved 94% precision with 38% lower fraud losses. Implemented Kafka + Spark structured streaming scoring for 25M+ daily transactions with sub-200ms latency and reduced false-positive fraud alerts by 45%. Implemented DBSCAN, K-Means, One-Class SVM, and graph analytics for behavioral profiling and fraud ring identification; reduced investigation workload by 50%. Orchestrated MLOps
Machine Learning Engineer at Mphasis, India
January 1, 2021 - April 30, 2023Engineered XGBoost, LightGBM, and CatBoost credit-risk models with PySpark and AWS EMR, improving prediction accuracy to 91% and reducing defaults by 24%. Designed Spark-based feature engineering pipelines generating 500+ predictive attributes from UPI, GSTN, account aggregator, and banking data, increasing underwriting automation by 82%. Optimized loan decisioning workflows using Logistic Regression, PCA, Optuna tuning, and MLflow, reducing approval turnaround time from 48 hours to under 5 minutes. Developed real-time fraud detection models using XGBoost, Random Forest, Isolation Forest, and SHAP explainability, achieving 94% precision and reducing fraud losses by 38%. Built Kafka and Spark structured streaming scoring pipelines processing 25M+ daily transactions while maintaining sub-200ms latency and reducing false-positive fraud alerts by 45%. Implemented DBSCAN, K-means, One-Class SVM, and graph analytics for behavioral profiling and fraud ring identification, reducing investigati
Data Scientist at Streebo Inc
January 1, 2020 - December 1, 2020Analyzed 50M+ banking interaction records using Python, SQL, pandas, Spark, and EDA techniques, reducing customer churn by 21% via predictive behavioral modeling. Developed churn models using XGBoost, Random Forest, and Gradient Boosting with scikit-learn and Spark, increasing retention by 18% across digital banking channels. Built 300+ customer intelligence features from UPI, CRM, wallet, and banking datasets, improving campaign conversion rates by 30% through personalized recommendation analytics. Designed next-best-action recommendation models using customer segmentation, K-Means clustering, PCA, and statistical analysis, increasing digital banking adoption by 26%. Evaluated loan default risks using XGBoost, Logistic Regression, and Random Forest on 25M+ lending records, improving predictive accuracy to 89%. Engineered 400+ borrower-risk indicators using Python, Spark, PCA, and feature selection, reducing delinquency by 17% and underwriting effort by 45%. Delivered Tableau/Power BI
Data Scientist at Streebo Inc, India
January 1, 2020 - December 31, 2020Analyzed 50M+ banking interaction records using Python, SQL, pandas, Spark, and EDA techniques, reducing customer churn by 21% via predictive behavioral modeling. Developed churn models using XGBoost, Random Forest, and Gradient Boosting with scikit-learn and Spark, increasing customer retention by 18% across digital banking channels. Built 300+ customer intelligence features from UPI, CRM, wallet, and banking datasets, improving campaign conversion rates by 30% through personalized recommendation analytics. Designed next-best-action recommendation models using customer segmentation, K-means clustering, PCA, and statistical analysis, increasing digital banking adoption by 26%. Evaluated loan default risks on 25M+ lending records using XGBoost, Logistic Regression, and Random Forest, improving prediction accuracy to 89%. Engineered 400+ borrower-risk indicators using Python, Spark, PCA, and feature selection, reducing delinquency rate by 17% and underwriting effort by 45%. Delivered Tab
Education
Master of Science in Information Systems at Indian a Tech University , USA
January 11, 2030 - March 20, 2025Master of Science in Information Systems at Indian a Tech University
January 1, 2025 - July 23, 2026Master of Science in Information Systems at Indian a Tech University , USA
January 11, 2030 - March 20, 2025Qualifications
Industry Experience
Financial Services
Skills
Experience Level
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
Intermediate
Intermediate
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