Veerendra Sai

Machine Learning Engineer

Bengaluru, Karnataka, India6 yrs 7 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Improved risk prediction accuracy by 15% in Fintech.
Enhanced search relevance by 35% through data analysis.
Implemented real-time analytics, reducing response latency by 30%.

Stackforce AI infers this person is a Machine Learning Engineer with expertise in Fintech and Data Engineering.

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Skills

Core Skills

Machine LearningData ScienceData EngineeringSoftware DevelopmentAutomationSystem EngineeringWeb Development

Other Skills

AlgorithmsAnalytical SkillsApache SparkArtificial Intelligence (AI)Automation ScriptsBig DataCluster AnalysisCode OptimizationCredit Risk ModelingData AnalysisData AnalyticsData MiningData PipelinesData ProcessingData Structures

About

Experienced Machine learning Engineer with a demonstrated history of working in the information technology, services and banking industry. Skilled in Python Programming Language, SQL, Agile Methodologies, Data Migration, Data Engineering and Linux. Experienced in all the phases of software development life cycle, that includes Requirement Study, Analysis, Design, Development, Integration and Implementation of various client/server applications

Experience

6 yrs 7 mos

Total Experience

1 yr 1 mo

Average Tenure

1 yr 3 mos

Current Experience

Google

Software Engineer IV - Machine Learning

Mar 2025 – Present · 1 yr 3 mos

Tavant

Data Scientist

May 2024 – Feb 2025 · 9 mos · Hybrid

Client - Transunion(CIBIL)
Credit Risk Modeling & ML Registry
Processed large-scale financial data to identify key risk factors, improving risk prediction accuracy by 15%.
Designed feature engineering pipelines to extract structured insights from historical records, enabling better decisioning.

Credit Risk ModelingMachine LearningData ProcessingFeature EngineeringData Science

Jio

Senior Machine Learning Engineer

Dec 2021 – Apr 2024 · 2 yrs 4 mos · Bengaluru, Karnataka, India · On-site

Analyzed user query data to improve search relevance by 35%, optimizing content discovery and retrieval efficiency.
Designed query processing pipelines to detect user behavior trends, enabling real-time personalized recommendations.
Additional Contributions
Implemented real-time analytics for Live TV Monitoring, reducing response latency by 30% and improving content targeting.
Developed structured data pipelines for ML models, ensuring high-quality data processing and governance for recommendation analytics.

Data AnalysisQuery ProcessingReal-time AnalyticsData PipelinesMachine LearningData Engineering