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I am Sai Satish Chandra Piske, an AI/ML Engineer with 4+ years of experience designing, developing, and deploying scalable machine learning solutions using Python, SQL, and PySpark. I specialize in building end-to-end ML pipelines—from data ingestion and feature engineering to model training, validation, and production deployment. I also excel at translating complex data insights into actionable dashboards and collaborating with cross-functional teams in Agile environments. I work with supervised learning, time-series forecasting (ARIMA, LSTM), anomaly detection, and predictive modeling with Random Forest and XGBoost. I’ve delivered ML and Generative AI solutions on AWS with Docker, integrated outputs into Tableau dashboards, and supported risk, compliance, and quantitative teams to align models with governance and business objectives.…I am Sai Satish Chandra Piske, an AI/ML Engineer with 4+ years of experience designing, developing, and deploying scalable machine learning solutions using Python, SQL, and PySpark. I specialize in building end-to-end ML pipelines—from data ingestion and feature engineering to model training, validation, and production deployment. I also excel at translating complex data insights into actionable dashboards and collaborating with cross-functional teams in Agile environments. I work with supervised learning, time-series forecasting (ARIMA, LSTM), anomaly detection, and predictive modeling with Random Forest and XGBoost. I’ve delivered ML and Generative AI solutions on AWS with Docker, integrated outputs into Tableau dashboards, and supported risk, compliance, and quantitative teams to align models with governance and business objectives.

Sai Satish Chandra Piske

AI Engineer, Data Scientist, Data Analyst, +3

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I am Sai Satish Chandra Piske, an AI/ML Engineer with 4+ years of experience designing, developing, and deploying scalable machine learning solutions using Python, SQL, and PySpark. I specialize in building end-to-end ML pipelines—from data ingestion and feature engineering to model training, validation, and production deployment. I also excel at translating complex data insights into actionable dashboards and collaborating with cross-functional teams in Agile environments. I work with supervised learning, time-series forecasting (ARIMA, LSTM), anomaly detection, and predictive modeling with Random Forest and XGBoost. I’ve delivered ML and Generative AI solutions on AWS with Docker, integrated outputs into Tableau dashboards, and supported risk, compliance, and quantitative teams to align models with governance and business objectives.…I am Sai Satish Chandra Piske, an AI/ML Engineer with 4+ years of experience designing, developing, and deploying scalable machine learning solutions using Python, SQL, and PySpark. I specialize in building end-to-end ML pipelines—from data ingestion and feature engineering to model training, validation, and production deployment. I also excel at translating complex data insights into actionable dashboards and collaborating with cross-functional teams in Agile environments. I work with supervised learning, time-series forecasting (ARIMA, LSTM), anomaly detection, and predictive modeling with Random Forest and XGBoost. I’ve delivered ML and Generative AI solutions on AWS with Docker, integrated outputs into Tableau dashboards, and supported risk, compliance, and quantitative teams to align models with governance and business objectives.

Available to hire

I am Sai Satish Chandra Piske, an AI/ML Engineer with 4+ years of experience designing, developing, and deploying scalable machine learning solutions using Python, SQL, and PySpark. I specialize in building end-to-end ML pipelines—from data ingestion and feature engineering to model training, validation, and production deployment. I also excel at translating complex data insights into actionable dashboards and collaborating with cross-functional teams in Agile environments. I work with supervised learning, time-series forecasting (ARIMA, LSTM), anomaly detection, and predictive modeling with Random Forest and XGBoost. I’ve delivered ML and Generative AI solutions on AWS with Docker, integrated outputs into Tableau dashboards, and supported risk, compliance, and quantitative teams to align models with governance and business objectives.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI/ML Engineer at State Street, USA

November 1, 2024 - Present

Designed and developed ML models (Python, Scikit-learn, XGBoost) to predict portfolio risk indicators, volatility trends, and abnormal asset movements across large-scale financial datasets. Built end-to-end data pipelines (SQL, Pandas, PySpark) to process market data and pricing from internal and external banking systems. Implemented time-series forecasting (ARIMA, LSTM). Developed anomaly detection and compliance monitoring using Isolation Forest and statistical thresholds. Built Generative AI solutions using LLMs, NLP, and LangChain to automate financial document analysis, risk report summarization, and policy interpretation. Built Retrieval-Augmented Generation (RAG) pipelines for QA from internal policies and audits. Deployed on AWS (S3, EC2, SageMaker) with Docker. Integrated outputs into Tableau dashboards. Worked in Agile with risk, compliance, and quant teams.

Machine Learning Engineer at Hexaware Technologies

July 1, 2019 - December 1, 2022

Developed ML models for banking transaction data to generate customer-level risk scores; built robust data pipelines (PySpark, SQL, Pandas) to ingest, cleanse, and transform high-volume transactional data from multiple financial systems. Engineered time-based and behavioral features to improve credit and risk model accuracy. Implemented supervised models (Logistic Regression, Random Forest, XGBoost); applied anomaly detection for suspicious transactions and compliance. Supported NLP and Generative AI use cases for financial documents and policy analysis; deployed ML solutions using Docker on AWS (EC2, S3, Lambda); collaborated with stakeholders in Agile teams to validate models and monitor performance.

AI/ML Engineer at State Street

November 1, 2024 - Present

Designed and developed machine learning models using Python, Scikit-learn, and XGBoost to predict portfolio risk indicators, volatility trends, and abnormal asset movements across large-scale financial datasets. Built and optimized end-to-end data pipelines using SQL, Pandas, and PySpark to process high-volume market data, transaction logs, and historical pricing data from internal and external banking systems. Implemented time-series forecasting models (ARIMA, LSTM) to analyze asset performance trends and support proactive portfolio risk mitigation strategies. Developed anomaly detection and compliance monitoring frameworks using Isolation Forest and statistical thresholding to identify unusual trading behavior and potential regulatory risks. Designed and implemented Generative AI solutions using LLMs, NLP, and LangChain to automate financial document analysis, risk report summarization, and regulatory policy interpretation. Built Retrieval-Augmented Generation (RAG) pipelines enablin