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I’m Vineen Kumar Gorre, an AI/ML Engineer with 3.5+ years of experience building, deploying, and monitoring deep learning models in production environments. I work end-to-end across the ML lifecycle—designing scalable data pipelines, setting up training and model registration flows, and ensuring reliable, reproducible deployments. I’m comfortable troubleshooting performance and reliability issues in production and enjoy collaborating with engineering, product, and business stakeholders to turn ambiguous AI questions into measurable, production-ready solutions. My focus spans MLOps (Docker, Kubernetes, CI/CD, MLflow) and cloud deployment (AWS services), along with practical work in event-driven inference and workflow automation.…I’m Vineen Kumar Gorre, an AI/ML Engineer with 3.5+ years of experience building, deploying, and monitoring deep learning models in production environments. I work end-to-end across the ML lifecycle—designing scalable data pipelines, setting up training and model registration flows, and ensuring reliable, reproducible deployments. I’m comfortable troubleshooting performance and reliability issues in production and enjoy collaborating with engineering, product, and business stakeholders to turn ambiguous AI questions into measurable, production-ready solutions. My focus spans MLOps (Docker, Kubernetes, CI/CD, MLflow) and cloud deployment (AWS services), along with practical work in event-driven inference and workflow automation.

Naveen G

AI Engineer, Data Engineer, DevOps Developer, +4

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I’m Vineen Kumar Gorre, an AI/ML Engineer with 3.5+ years of experience building, deploying, and monitoring deep learning models in production environments. I work end-to-end across the ML lifecycle—designing scalable data pipelines, setting up training and model registration flows, and ensuring reliable, reproducible deployments. I’m comfortable troubleshooting performance and reliability issues in production and enjoy collaborating with engineering, product, and business stakeholders to turn ambiguous AI questions into measurable, production-ready solutions. My focus spans MLOps (Docker, Kubernetes, CI/CD, MLflow) and cloud deployment (AWS services), along with practical work in event-driven inference and workflow automation.…I’m Vineen Kumar Gorre, an AI/ML Engineer with 3.5+ years of experience building, deploying, and monitoring deep learning models in production environments. I work end-to-end across the ML lifecycle—designing scalable data pipelines, setting up training and model registration flows, and ensuring reliable, reproducible deployments. I’m comfortable troubleshooting performance and reliability issues in production and enjoy collaborating with engineering, product, and business stakeholders to turn ambiguous AI questions into measurable, production-ready solutions. My focus spans MLOps (Docker, Kubernetes, CI/CD, MLflow) and cloud deployment (AWS services), along with practical work in event-driven inference and workflow automation.

Available to hire

I’m Vineen Kumar Gorre, an AI/ML Engineer with 3.5+ years of experience building, deploying, and monitoring deep learning models in production environments. I work end-to-end across the ML lifecycle—designing scalable data pipelines, setting up training and model registration flows, and ensuring reliable, reproducible deployments.

I’m comfortable troubleshooting performance and reliability issues in production and enjoy collaborating with engineering, product, and business stakeholders to turn ambiguous AI questions into measurable, production-ready solutions. My focus spans MLOps (Docker, Kubernetes, CI/CD, MLflow) and cloud deployment (AWS services), along with practical work in event-driven inference and workflow automation.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Beginner

Beginner

Language

English

Advanced

Hindi

Intermediate

German

Intermediate

Work Experience

AI/ML Engineer at Harve st AI

December 1, 2025 - Present

Built end-to-end prediction pipelines, containerized and deployed ML/DL models using Docker, GitHub Actions, and AWS ECS, with CI/CD to automate training and ensure reproducible deployments. Automated ingestion of sensor and operational data via periodic Airflow ETL jobs. Implemented standardized MLOps pipelines with Docker, GitHub Actions, and AWS ECS for CI/CD and reproducible training, including MLflow-based model registration and lifecycle management. Worked on high-resolution camera feeder pipelines using OpenCV to extract structured information from video/image streams and monitor production-critical metrics. Implemented event-driven training and inference workflows using AWS Lambda and SageMaker, integrating MLflow for auditability and reproducibility. Reduced forecasting error (SMAPE) from 31% to 18% via a rolling 6-week harvest yield forecasting pipeline in Python with SHAP-driven feature engineering. Also provisioned cloud infrastructure with Terraform and designed OCR pipeli

AI/ML Engineer at Harveast AI

December 1, 2025 - Present

Built end-to-end prediction pipelines that containerize and deploy machine learning and deep learning models using Docker, GitHub Actions, and AWS ECS, enabling CI/CD, automated model training, and reproducible deployments. Automated ingestion of sensor and operational data via periodic ETL jobs in Airflow, improving pipeline reliability and reducing manual data effort. Standardized model deployments by implementing end-to-end MLOps pipelines (Docker, GitHub Actions, AWS ECS) for consistent training and CI/CD. Improved forecast accuracy by reducing forecast error (SMAPE) from 31% to 18% using a rolling 6-week harvest yield forecasting pipeline in Python and enhanced feature engineering with SHAP. Worked on high-resolution camera feeder pipelines to extract structured information from video and image streams using OpenCV for robust analytics and production monitoring metrics. Implemented event-driven training and inference workflows with AWS Lambda and SageMaker and integrated MLflow fo

AI/ML Engineer at Harvest AI

December 1, 2025 - Present

Built end-to-end prediction pipelines and deployed ML/DL models using Docker, GitHub Actions, and AWS ECS, enabling CI/CD automation for training and reproducible deployments. Automated periodic sensor and operational data ingestion with Airflow ETL jobs. Implemented standardized MLOps pipelines (Docker, GitHub Actions, AWS ECS) with MLflow-based lifecycle management and auditability. Improved forecast error (SMAPE) from 31% to 18% using a rolling 6-week harvest yield forecasting pipeline in Python and enhanced feature engineering with SHAP. Worked with high-resolution camera-fed video/image pipelines (OpenCV) for structured information extraction and production-critical monitoring. Implemented event-driven training and inference workflows using AWS Lambda and SageMaker; reduced storage pressure and supply-demand mismatches by forecasting 2–4 weeks ahead; provisioned infrastructure with Terraform; developed OCR pipelines for extracting structured data from image/video inputs.

ML Engineer | Cloud Data Consultancy Services (TCS) at Cloud Data Consultancy Services (TCS)

September 1, 2022 - December 1, 2024

Designed and deployed end-to-end ML pipelines for financial customer segmentation and product prediction using Python and SQL on large-scale enterprise datasets, improving model precision by 18%. Achieved a 74% F1 score by building a recommendation system using gradient-boosted models (XGBoost/LightGBM) with structured feature engineering and Bayesian hyperparameter optimization. Reduced key data retrieval times by 20% by optimizing complex SQL queries using window functions, advanced joins, and partitioning, improving report refresh SLAs. Improved downstream data reusability using dimensional data models with slowly changing dimensions, snapshot patterns, and audit/lineage columns to support downstream flows to SAP systems. Ensured consistent inputs to BI and ML pipelines by implementing automated ETL workflows (Python, PySpark, SQL) to clean, validate, and reconcile transactional data across enterprise systems. Improved scalability and fault tolerance by containerizing ML inference s

ML Engineer | Cloud Data Consultancy Services (TCS) at Tata Consultancy Services (TCS)

September 1, 2022 - December 1, 2024

Designed and deployed end-to-end ML pipelines for financial customer segmentation and product prediction using Python and SQL on large-scale enterprise datasets, improving model precision by 18%. Achieved a 74% F1 score by building a recommendation system using gradient-boosted models (XGBoost/LightGBM) with structured feature engineering and Bayesian hyperparameter optimization. Reduced key data retrieval times by 20% by optimizing SQL queries via window functions, advanced joins, and partitioning. Improved downstream data reuse with dimensional modeling, slowly changing dimensions, snapshot patterns, and audit/lineage columns. Ensured consistent inputs to BI and ML pipelines with automated ETL workflows in Python/PySpark/SQL. Increased scalability and fault tolerance by containerizing ML inference services and deploying with Kubernetes via EKS. Reduced discrepancy resolution time by 14% using real-time anomaly detection pipelines across transaction streams. Partnered with BI develope

Data Engineer at Cloud Data Consultancy Services (TCS)

May 1, 2021 - September 1, 2022

Reduced data cleaning and preprocessing time by 25% by building PySpark-based ETL pipelines on Databricks to process structured and semi-structured (NoSQL) datasets. Improved lakehouse performance by reducing query latency by 20% through optimized SQL and Spark access patterns including partitioning and access-pattern tuning. Supported an 8% improvement in business process efficiency by building analytics dashboards and operational data models to surface KPIs for stakeholder decision-making. Improved data quality and reporting accuracy by developing anomaly and outlier detection pipelines using statistical techniques (Z-score) and MLlib’s Isolation Forest. Improved reliability of downstream reporting by scheduling and orchestrating recurring data transformation jobs to ensure consistent, on-time data availability for consumption. Increased stakeholder visibility into operational trends by representing insights through Tableau dashboards, enabling faster, more informed business decisi

Data Engineer at Tata Consultancy Services (TCS)

May 1, 2021 - September 1, 2022

Reduced data cleaning and preprocessing time by 25% by developing PySpark ETL pipelines on DataBricks for structured and semi-structured (NoSQL) datasets. Improved lakehouse query latency by 20% using optimized SQL and Spark access patterns, including partitioning and tuning. Supported an 8% improvement in business process efficiency by building analytical dashboards and operational data models surfacing KPIs. Improved data quality and reporting accuracy by creating anomaly and outlier detection pipelines using Z-score techniques and MLlib’s Isolation Forest. Improved reliability of downstream reporting by orchestrating and scheduling recurring data transformation jobs to ensure consistent on-time availability for consumption. Increased stakeholder visibility into operational trends through Tableau dashboards.

Education

Master of Science - Software Engineering at University of Europe, Germany

January 1, 2024 - January 1, 2026

Bachelor of Engineering - Mechanical Engineering at Osmania University, Hyderabad

January 1, 2015 - January 1, 2019

Master of Science at University of Europe

January 1, 2024 - January 1, 2026

Bachelor of Engineering at Osmania University

January 1, 2015 - January 1, 2019

Master of Science - Software Engineering at University of Europe, Germany

January 1, 2024 - January 1, 2026

Bachelor of Engineering - Mechanical Engineering at Osmania University, Hyderabad

January 1, 2015 - January 1, 2019

Master of Science - Software Engineering at University of Europe

January 1, 2024 - January 1, 2026

Bachelor of Engineering - Mechanical Engineering at Osmania University

January 1, 2015 - January 1, 2019

Statistics and Machine Learning Specialization at John Hopkins University (Coursera)