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Hi, I’m Piyush Patil, a Machine Learning Engineer with 6+ years of experience designing and deploying production-grade ML systems, LLM-powered applications, and high-throughput data pipelines. I’ve delivered measurable outcomes in mobility forecasting, enterprise risk analytics, and audit intelligence at Uber and KPMG, translating business needs into predictive models, time-series systems, and anomaly detection frameworks. I specialize in end-to-end ML ops, fine-tuning LoRA/QLoRA, agentic AI workflows, and vector search using LangChain, LangGraph, OpenAI API, and Hugging Face. Proficient with Airflow, Docker, Kubernetes, MLflow, and CI/CD across AWS, GCP, and Azure, I bring hands-on experience with PySpark, Delta Lake, and data validation with Great Expectations and DVC to enable reliable, scalable ML solutions.…Hi, I’m Piyush Patil, a Machine Learning Engineer with 6+ years of experience designing and deploying production-grade ML systems, LLM-powered applications, and high-throughput data pipelines. I’ve delivered measurable outcomes in mobility forecasting, enterprise risk analytics, and audit intelligence at Uber and KPMG, translating business needs into predictive models, time-series systems, and anomaly detection frameworks. I specialize in end-to-end ML ops, fine-tuning LoRA/QLoRA, agentic AI workflows, and vector search using LangChain, LangGraph, OpenAI API, and Hugging Face. Proficient with Airflow, Docker, Kubernetes, MLflow, and CI/CD across AWS, GCP, and Azure, I bring hands-on experience with PySpark, Delta Lake, and data validation with Great Expectations and DVC to enable reliable, scalable ML solutions.

Piyush Patil

AI Engineer, Data Scientist, Data Analyst, +2

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Hi, I’m Piyush Patil, a Machine Learning Engineer with 6+ years of experience designing and deploying production-grade ML systems, LLM-powered applications, and high-throughput data pipelines. I’ve delivered measurable outcomes in mobility forecasting, enterprise risk analytics, and audit intelligence at Uber and KPMG, translating business needs into predictive models, time-series systems, and anomaly detection frameworks. I specialize in end-to-end ML ops, fine-tuning LoRA/QLoRA, agentic AI workflows, and vector search using LangChain, LangGraph, OpenAI API, and Hugging Face. Proficient with Airflow, Docker, Kubernetes, MLflow, and CI/CD across AWS, GCP, and Azure, I bring hands-on experience with PySpark, Delta Lake, and data validation with Great Expectations and DVC to enable reliable, scalable ML solutions.…Hi, I’m Piyush Patil, a Machine Learning Engineer with 6+ years of experience designing and deploying production-grade ML systems, LLM-powered applications, and high-throughput data pipelines. I’ve delivered measurable outcomes in mobility forecasting, enterprise risk analytics, and audit intelligence at Uber and KPMG, translating business needs into predictive models, time-series systems, and anomaly detection frameworks. I specialize in end-to-end ML ops, fine-tuning LoRA/QLoRA, agentic AI workflows, and vector search using LangChain, LangGraph, OpenAI API, and Hugging Face. Proficient with Airflow, Docker, Kubernetes, MLflow, and CI/CD across AWS, GCP, and Azure, I bring hands-on experience with PySpark, Delta Lake, and data validation with Great Expectations and DVC to enable reliable, scalable ML solutions.

Available to hire

Hi, I’m Piyush Patil, a Machine Learning Engineer with 6+ years of experience designing and deploying production-grade ML systems, LLM-powered applications, and high-throughput data pipelines. I’ve delivered measurable outcomes in mobility forecasting, enterprise risk analytics, and audit intelligence at Uber and KPMG, translating business needs into predictive models, time-series systems, and anomaly detection frameworks.

I specialize in end-to-end ML ops, fine-tuning LoRA/QLoRA, agentic AI workflows, and vector search using LangChain, LangGraph, OpenAI API, and Hugging Face. Proficient with Airflow, Docker, Kubernetes, MLflow, and CI/CD across AWS, GCP, and Azure, I bring hands-on experience with PySpark, Delta Lake, and data validation with Great Expectations and DVC to enable reliable, scalable ML solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI/ML Engineer at Uber

September 1, 2025 - Present

Engineered LSTM-based demand forecasting models in PyTorch across 80K+ daily ride requests, tracking experiments with weights & biases to systematically improve accuracy during peak-demand periods. Orchestrated end-to-end ML pipelines using PySpark, Databricks, Delta Lake, and Docker to ingest and transform 35GB+ weekly ride and telemetry data, increasing forecast refresh frequency and reducing retraining overhead. Developed surge pricing prediction models using XGBoost with advanced feature engineering and hyperparameter tuning to estimate fare-adjustment patterns across 3 metropolitan regions. Constructed edge-geospatial demand prediction models with H3 indexing and spatial feature engineering to pinpoint high-demand zones, reducing rider wait times by 12% in targeted areas. Versioned 120K+ driver feedback records with DVC and Great Expectations for data validation, producing consistent, schema-validated training datasets for downstream NLP workflows. Fine-tuned transformer-based lan

Data Scientist at KPMG

January 1, 2021 - July 1, 2023

Surfaced 2,150 anomalous patterns across 9 enterprise engagements by training risk prediction models with Scikit-learn, logistic regression, and statistical modeling on 420K+ transactional records. Established data quality standards by implementing validation frameworks using dbt, Great Expectations, and rule-based PySpark checks across 35+ large-scale datasets during quarterly audit cycles. Improved high-risk entity identification by deploying gradient boosting and decision tree classification models on 180K+ structured records, delivering measurable gains in precision and recall. Eliminated 8 hours of manual effort per reporting cycle by re-engineering ETL pipelines with Airflow, dbt, and Pandas, consolidating 62GB of data from 7 disparate source systems. Strengthened audit coverage via Isolation Forest anomaly detection and statistical outlier analysis across 275K+ records, flagging 3,480 irregular patterns for investigative review. Accelerated review turnaround by 2 business days b

Data Analyst at Trinity Technolabs

August 1, 2017 - December 1, 2020

Interrogated 180K+ client and transaction records with SQL and data aggregation techniques, uncovering 1,100+ reporting inconsistencies and improving monthly business reporting accuracy. Launched Power BI dashboards and DAX reports for business operations and client KPIs, producing 10+ dashboards and reducing manual reporting effort by 5 hours per week. Audited 15+ datasets through Excel VBA, data reconciliation, and control checks, resolving 800+ mismatched records to improve downstream reporting reliability. Streamlined data extraction and transformation with Python, Pandas, and ETL pipelines, processing 30GB+ structured business data and trimming weekly preparation time by 6 hours. Generated trend analysis reports with Excel forecasting models and time-series techniques across 24+ months of production data to support planning and performance tracking. Applied linear regression and feature selection via Scikit-learn on 90K+ transaction records to expose cost and revenue variance patt