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I am a data specialist focused on AI data engineering and data science, with hands-on experience turning data into actionable insights. I’ve driven model accuracy improvements (e.g., 22% in production) and boosted analytics speed by designing scalable ETL pipelines in cloud environments, while automating data workflows to cut manual processing and costs. I’m passionate about applying feature engineering, ML modeling, and robust data architectures to deliver measurable business value for data-driven teams. I’m excited to continue building end-to-end data platforms and GenAI-ready data ecosystems, collaborating with stakeholders to align solutions with business goals and deliver rapid, reliable analytics and ML outcomes.…I am a data specialist focused on AI data engineering and data science, with hands-on experience turning data into actionable insights. I’ve driven model accuracy improvements (e.g., 22% in production) and boosted analytics speed by designing scalable ETL pipelines in cloud environments, while automating data workflows to cut manual processing and costs. I’m passionate about applying feature engineering, ML modeling, and robust data architectures to deliver measurable business value for data-driven teams. I’m excited to continue building end-to-end data platforms and GenAI-ready data ecosystems, collaborating with stakeholders to align solutions with business goals and deliver rapid, reliable analytics and ML outcomes.

Dinesh Krishna Goriparthi

Data Scientist, AI Engineer, AI Developer, +2

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I am a data specialist focused on AI data engineering and data science, with hands-on experience turning data into actionable insights. I’ve driven model accuracy improvements (e.g., 22% in production) and boosted analytics speed by designing scalable ETL pipelines in cloud environments, while automating data workflows to cut manual processing and costs. I’m passionate about applying feature engineering, ML modeling, and robust data architectures to deliver measurable business value for data-driven teams. I’m excited to continue building end-to-end data platforms and GenAI-ready data ecosystems, collaborating with stakeholders to align solutions with business goals and deliver rapid, reliable analytics and ML outcomes.…I am a data specialist focused on AI data engineering and data science, with hands-on experience turning data into actionable insights. I’ve driven model accuracy improvements (e.g., 22% in production) and boosted analytics speed by designing scalable ETL pipelines in cloud environments, while automating data workflows to cut manual processing and costs. I’m passionate about applying feature engineering, ML modeling, and robust data architectures to deliver measurable business value for data-driven teams. I’m excited to continue building end-to-end data platforms and GenAI-ready data ecosystems, collaborating with stakeholders to align solutions with business goals and deliver rapid, reliable analytics and ML outcomes.

Available to hire

I am a data specialist focused on AI data engineering and data science, with hands-on experience turning data into actionable insights. I’ve driven model accuracy improvements (e.g., 22% in production) and boosted analytics speed by designing scalable ETL pipelines in cloud environments, while automating data workflows to cut manual processing and costs. I’m passionate about applying feature engineering, ML modeling, and robust data architectures to deliver measurable business value for data-driven teams.

I’m excited to continue building end-to-end data platforms and GenAI-ready data ecosystems, collaborating with stakeholders to align solutions with business goals and deliver rapid, reliable analytics and ML outcomes.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

Afar

Advanced

Work Experience

AI Data Engineer at PHX Agile LLC

March 31, 2025 - March 31, 2025

Led end-to-end AI data engineering efforts, boosting model accuracy by 22% through feature engineering and hyperparameter tuning across supervised algorithms in Python. Designed scalable ETL pipelines in Azure Synapse Analytics processing 5+ TB daily, reducing preprocessing time by 30%. Automated data preparation workflows with Python in Azure Data Factory, saving ~$35K annually. Advocated Agile/Scrum practices, improving delivery speed by 20%. Built feature engineering scripts (Pandas, NumPy, PySpark) to enhance training efficiency and cut data latency by 40%. Implemented schema design and OLAP data models to support batch and near real-time Spark streaming with agentic GenAI workflows, ensuring data quality via Azure Functions and Key Vault RBAC. Improved churn prediction by 18% through ensemble models and SHAP explainability. Automated data cleaning in R and Python, cutting preprocessing time by 40%. Optimized SQL for large daily reports (>10M records) by 35%. Processed over 2 TB of

Associate Software Engineer at TCS

August 31, 2023 - August 31, 2023

Accelerated delivery by 25% for 100+ clients by building real-time ETL DataStage pipelines with AWS integrations. Architected, developed, and maintained ETL pipelines with AutoSys scheduling for monitoring and support. Enhanced deployments by 15% through CI/CD pipelines and Git/TFS version control. Migrated DataStage apps and Oracle databases from on-prem to AWS with zero data loss, ensuring high availability and low latency. Provided production support resolving 90% of job failures within SLA using AutoSys. Participated in cross-functional teams to improve features by 20%, addressing evolving business needs. Migrated on-prem SQL Server workloads to AWS EC2/S3 and redesigned batch workflows. Recognized as Star of the Month twice for dedication and stakeholder impact.

Junior Engineer, Data Network and Telecommunications at South Central Railway

April 30, 2021 - April 30, 2021

Resolved 15+ network issues with a 95% first-attempt resolution rate, reducing network downtime by 30%. Improved system reliability by 20% through data network and CRIS project work. Applied software and security updates adhering to policies. Raised dashboard adoption by 25% by consolidating server health metrics. Contributed to a 10% increase in operational efficiency by reducing downtime.