Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

AI/ML Engineer with 3 years of experience building machine learning and generative AI solutions across banking, fraud detection, KYC, manufacturing quality, and predictive maintenance. Skilled in feature engineering, leakage-aware validation, retrieval evaluation, API deployment, and model monitoring with responsible AI practices. Delivered Python-based services using cloud, containers, streaming, and MLOps, focusing on reliable production releases, measurable model performance, and strong collaboration with compliance, engineering, and business teams.…AI/ML Engineer with 3 years of experience building machine learning and generative AI solutions across banking, fraud detection, KYC, manufacturing quality, and predictive maintenance. Skilled in feature engineering, leakage-aware validation, retrieval evaluation, API deployment, and model monitoring with responsible AI practices. Delivered Python-based services using cloud, containers, streaming, and MLOps, focusing on reliable production releases, measurable model performance, and strong collaboration with compliance, engineering, and business teams.

Jigar Purohit

AI Engineer, Data Scientist, Software Engineer, +2





AI/ML Engineer with 3 years of experience building machine learning and generative AI solutions across banking, fraud detection, KYC, manufacturing quality, and predictive maintenance. Skilled in feature engineering, leakage-aware validation, retrieval evaluation, API deployment, and model monitoring with responsible AI practices. Delivered Python-based services using cloud, containers, streaming, and MLOps, focusing on reliable production releases, measurable model performance, and strong collaboration with compliance, engineering, and business teams.…AI/ML Engineer with 3 years of experience building machine learning and generative AI solutions across banking, fraud detection, KYC, manufacturing quality, and predictive maintenance. Skilled in feature engineering, leakage-aware validation, retrieval evaluation, API deployment, and model monitoring with responsible AI practices. Delivered Python-based services using cloud, containers, streaming, and MLOps, focusing on reliable production releases, measurable model performance, and strong collaboration with compliance, engineering, and business teams.

Available to hire

AI/ML Engineer with 3 years of experience building machine learning and generative AI solutions across banking, fraud detection, KYC, manufacturing quality, and predictive maintenance. Skilled in feature engineering, leakage-aware validation, retrieval evaluation, API deployment, and model monitoring with responsible AI practices.

Delivered Python-based services using cloud, containers, streaming, and MLOps, focusing on reliable production releases, measurable model performance, and strong collaboration with compliance, engineering, and business teams.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Language

Work Experience

AI/ML Engineer at Citigroup Inc.

January 1, 2026 - Present

Designed an agentic KYC onboarding workflow using Python, LangGraph, FastAPI, pgvector, and Neo4j to extract documents, validate ownership data, and support compliance reviews. Integrated Hugging Face Transformers, PostgreSQL, Kafka, and Redis for document classification, workflow state management, policy retrieval, and full audit trails. Improved analyst productivity by 20% using grounded RAG summaries with source citations, human approvals, prompt injection security controls, and MLflow tracking with role-based access. Engineered a real-time payment fraud pipeline using Kafka, PySpark, Feast, Redis, LightGBM, autoencoders, and GraphSAGE for low-latency unusual payment scoring. Reduced false positives by 30% via a low-latency risk ensemble using behavioral features, graph relationships, anomaly scores, SHAP explanations, and client-specific thresholds. Orchestrated production deployments using Docker, Kubernetes, GitLab CI, MLflow, Prometheus/Grafana, and OpenTelemetry with Evidently-

Associate Data Scientist at Bosch

January 1, 2022 - July 1, 2024

Built manufacturing quality prediction models using Python, scikit-learn, and XGBoost with feature engineering to detect defect patterns and support production decision-making. Increased accuracy by 2.2% by refining production features, validating XGBoost performance, and benchmarking across equipment and process conditions. Automated ETL pipelines using Python, SQL, Pandas, and PySpark to consolidate sensor, inspection, and process records. Reduced data preparation time by 40% by standardizing ETL workflows and optimizing PySpark transformations for high-volume manufacturing data. Strengthened model reliability through improved preprocessing, feature selection, and hyperparameter tuning workflows. Embedded models into enterprise applications using FastAPI and Docker for real-time quality predictions and operational insights. Produced visualizations with Plotly/Jupyter and supported controlled releases via Agile testing, Git reviews, documentation, and stakeholder feedback.