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I am a results-driven AI/ML engineer with 5+ years of experience designing and deploying scalable machine learning solutions, data pipelines, and cloud-native applications using Python and SQL. I specialize in feature engineering, predictive modeling, anomaly detection, and end-to-end ML lifecycle from data ingestion to production deployment. I have a proven track record of building ML-powered systems, optimizing data workflows, and delivering business-impact solutions using AWS, MLOps, and real-time APIs.\n\nI work with large structured and unstructured datasets to translate complex problems into production-ready AI solutions. I collaborate closely with product managers, stakeholders, and engineering teams to ship reliable models, ensure data privacy and governance, and drive measurable outcomes across cloud platforms (AWS/Azure/GCP).…I am a results-driven AI/ML engineer with 5+ years of experience designing and deploying scalable machine learning solutions, data pipelines, and cloud-native applications using Python and SQL. I specialize in feature engineering, predictive modeling, anomaly detection, and end-to-end ML lifecycle from data ingestion to production deployment. I have a proven track record of building ML-powered systems, optimizing data workflows, and delivering business-impact solutions using AWS, MLOps, and real-time APIs.\n\nI work with large structured and unstructured datasets to translate complex problems into production-ready AI solutions. I collaborate closely with product managers, stakeholders, and engineering teams to ship reliable models, ensure data privacy and governance, and drive measurable outcomes across cloud platforms (AWS/Azure/GCP).

Hari Sai Jogesh Veeramalla

Data Scientist, AI Engineer, Full Stack Developer, +6

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I am a results-driven AI/ML engineer with 5+ years of experience designing and deploying scalable machine learning solutions, data pipelines, and cloud-native applications using Python and SQL. I specialize in feature engineering, predictive modeling, anomaly detection, and end-to-end ML lifecycle from data ingestion to production deployment. I have a proven track record of building ML-powered systems, optimizing data workflows, and delivering business-impact solutions using AWS, MLOps, and real-time APIs.\n\nI work with large structured and unstructured datasets to translate complex problems into production-ready AI solutions. I collaborate closely with product managers, stakeholders, and engineering teams to ship reliable models, ensure data privacy and governance, and drive measurable outcomes across cloud platforms (AWS/Azure/GCP).…I am a results-driven AI/ML engineer with 5+ years of experience designing and deploying scalable machine learning solutions, data pipelines, and cloud-native applications using Python and SQL. I specialize in feature engineering, predictive modeling, anomaly detection, and end-to-end ML lifecycle from data ingestion to production deployment. I have a proven track record of building ML-powered systems, optimizing data workflows, and delivering business-impact solutions using AWS, MLOps, and real-time APIs.\n\nI work with large structured and unstructured datasets to translate complex problems into production-ready AI solutions. I collaborate closely with product managers, stakeholders, and engineering teams to ship reliable models, ensure data privacy and governance, and drive measurable outcomes across cloud platforms (AWS/Azure/GCP).

Available to hire

I am a results-driven AI/ML engineer with 5+ years of experience designing and deploying scalable machine learning solutions, data pipelines, and cloud-native applications using Python and SQL. I specialize in feature engineering, predictive modeling, anomaly detection, and end-to-end ML lifecycle from data ingestion to production deployment. I have a proven track record of building ML-powered systems, optimizing data workflows, and delivering business-impact solutions using AWS, MLOps, and real-time APIs.\n\nI work with large structured and unstructured datasets to translate complex problems into production-ready AI solutions. I collaborate closely with product managers, stakeholders, and engineering teams to ship reliable models, ensure data privacy and governance, and drive measurable outcomes across cloud platforms (AWS/Azure/GCP).

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Language

Javanese

Advanced

English

Fluent

Work Experience

AI/ML Engineer / Software Developer at Public Partnerships

February 1, 2025 - Present

Designed and implemented scalable ML-ready data pipelines using Python, SQL, and Airflow for ingesting, transforming, and validating large structured datasets. Built and deployed predictive models for classification, anomaly detection, and operational forecasting; performed feature engineering and model optimization to improve accuracy and reduce drift. Built and productionized ML models as RESTful and gRPC-based microservices for real-time inference. Deployed ML workloads on AWS, and developed CI/CD pipelines for automated training and deployment. Implemented data validation frameworks and monitoring to track performance and data drift across production systems. Collaborated with product and analytics teams to translate business problems into AI/ML solutions.

Graduate Research Assistant (AI/Data) at Kennesaw State University

January 1, 2023 - May 1, 2023

Conducted exploratory data analysis on healthcare and privacy datasets, performed data preprocessing and feature engineering, and developed predictive ML models for healthcare analytics. Built Python-based experimentation pipelines for model training and validation; applied statistical modeling and hypothesis testing to derive insights; created visualizations to communicate findings to stakeholders.

Associate System Engineer (Data & AI) at Tata Consultancy Services

July 1, 2019 - June 1, 2022

Designed and developed enterprise-scale data pipelines using Python, SQL, Airflow, and Spark; built automation scripts and ETL workflows to streamline data processing. Performed data cleaning and feature engineering; supported data science teams by building data transformation pipelines. Worked with distributed data processing frameworks; optimized inference latency and throughput; designed and maintained containerized AI workloads with Docker and Kubernetes; implemented anomaly detection and analytics solutions. Deployed backend services and APIs, integrated Kafka and DBT for real-time data workflows; participated in Agile ceremonies and documentation.

Engineering Intern at Doordarshan

January 1, 2019 - April 1, 2019

Developed Python automation scripts to support operational workflows, data handling, and system validation tasks. Monitored real-time broadcast and backend systems, analyzed data logs, and assisted in metadata management for media operations. Documented workflows and validated system behaviors, gaining exposure to large-scale real-time data pipelines and infrastructure monitoring.

Machine Learning Engineer - Generative AI at Public Partnerships

February 1, 2024 - Present

Designed and built Generative AI solutions leveraging LLMs to automate document processing, knowledge retrieval, and conversational workflows for internal and customer-facing applications. Implemented Retrieval-Augmented Generation pipelines by integrating vector databases and enterprise data sources to deliver context-aware responses. Led prompt engineering optimizations to improve accuracy, reduce hallucinations, and enhance reliability. Developed Python-based microservices and RESTful APIs for AI capabilities, established model monitoring, and implemented efficient inference strategies to balance latency and cost. Ensured data privacy and compliance through access controls and masking, and collaborated with product, data engineering, and security teams to deploy scalable AI solutions on cloud platforms (Azure/AWS/GCP).