I am an AI/ML Engineer with 5+ years of experience driving measurable business impact through applied ML. I delivered 18% fraud reduction, 15% retention improvement, and 12% higher campaign conversions by turning data into predictive insights. I excel at turning complex models into clear recommendations that executives can act on. I optimize processes through automation, embed fairness and privacy into production, and align AI initiatives with business goals to drive growth and reduce risk. I enjoy collaborating with cross-functional teams to translate business needs into technical solutions and continuously learn to stay ahead in the rapidly evolving AI landscape.…

Ramoji Jajam

I am an AI/ML Engineer with 5+ years of experience driving measurable business impact through applied ML. I delivered 18% fraud reduction, 15% retention improvement, and 12% higher campaign conversions by turning data into predictive insights. I excel at turning complex models into clear recommendations that executives can act on. I optimize processes through automation, embed fairness and privacy into production, and align AI initiatives with business goals to drive growth and reduce risk. I enjoy collaborating with cross-functional teams to translate business needs into technical solutions and continuously learn to stay ahead in the rapidly evolving AI landscape.…

Available to hire

I am an AI/ML Engineer with 5+ years of experience driving measurable business impact through applied ML. I delivered 18% fraud reduction, 15% retention improvement, and 12% higher campaign conversions by turning data into predictive insights. I excel at turning complex models into clear recommendations that executives can act on. I optimize processes through automation, embed fairness and privacy into production, and align AI initiatives with business goals to drive growth and reduce risk.

I enjoy collaborating with cross-functional teams to translate business needs into technical solutions and continuously learn to stay ahead in the rapidly evolving AI landscape.

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Skills

Experience Level

Work Experience

AI/ML Engineer at Capital One Financial
January 1, 2025 - Present
Decreased fraud false positives by 18% by building Python and Scikit-learn models in AWS SageMaker, improving monitoring accuracy across millions of financial transactions. Improved detection recalls by 22% through advanced feature engineering and parameter tuning, strengthening credit risk analysis. Cut fraud scoring latency from two hours to four minutes by deploying Dockerized Flask APIs integrated with enterprise decision platforms. Lowered model training costs by 20% by optimizing SageMaker compute resources and batch configurations for large-scale datasets. Embedded fairness metrics and privacy safeguards into production pipelines, ensuring compliance with Responsible AI standards. Delivered Tableau dashboards tracking drift, accuracy, and key fraud indicators, enabling leadership to act quickly on performance shifts. Automated retraining workflows through CI/CD, boosting production stability by 25%.
AI/ML Engineer at Mphasis, India
July 1, 2023 - October 15, 2025
Escalated customer retention by 15% by developing regression-based churn prediction models in Pandas and NumPy, analyzing large telecom subscriber datasets. Boosted customer revenue by 10% through classification models predicting upsell opportunities, enabling targeted cross-sell campaigns in retail banking. Improved text analysis accuracy by fine-tuning Hugging Face Transformers for multilingual sentiment classification across customer service data. Lessened experimentation cycles by 40% by automating Azure ML pipelines, accelerating validation and deployment for client ML solutions. Enhanced demand forecasting reliability 18% with ensemble methods and delivered predictive outputs directly into Power BI dashboards. Cut ETL preparation time by 35% by creating SQL preprocessing pipelines.
ML Engineer at Mphasis, India
August 1, 2021 - October 15, 2025
Lowered loan misclassification by 20% by deploying Gradient Boosting and Random Forest models on structured financial datasets. Heightened campaign conversion rates by 12% through K-Means clustering for segmentation of large transaction data. Improved preprocessing efficiency by automating feature engineering workflows with SQL and Pandas pipelines. Shortened deployment cycles by 50% through containerization with Docker and automated CI/CD delivery. Increased predictive accuracy through systematic feature selection, cross-validation, and parameter optimization. Enhanced credit scoring pipelines supporting ingestion of structured and semi-structured data at scale. Reduced manual data checks by automating preprocessing validation.
AI/ML Engineer at Capital One Financial
January 1, 2025 - November 25, 2025
Decreased fraud false positives by building Python and Scikit-learn models in AWS SageMaker, improving monitoring accuracy across millions of financial transactions. Improved detection recalls by 22% via feature engineering and parameter tuning. Cut fraud scoring latency from two hours to four minutes by deploying Dockerized Flask APIs integrated with enterprise decision platforms. Lowered model training costs by 20% through SageMaker compute/resource optimization. Embedded fairness metrics and privacy safeguards into production pipelines, ensuring compliance with federal standards on model governance and Responsible AI. Delivered Tableau dashboards tracking drift, accuracy, and key fraud indicators, enabling leadership to act quickly on performance shifts. Automated retraining workflows through CI/CD to enhance production model stability by 25%.
AI/ML Engineer at Mphasis
July 1, 2023 - July 1, 2023
Escalated customer retention by 15% by developing regression-based churn prediction models in Pandas and NumPy, analyzing large telecom subscriber datasets. Boosted customer revenue by 10% through classification models predicting upsell opportunities, enabling targeted cross-sell campaigns in retail banking. Improved text analysis accuracy by fine-tuning Hugging Face Transformers for multilingual sentiment classification. Reduced experimentation cycles by automating Azure ML pipelines, accelerating validation and deployment for client ML solutions. Enhanced demand forecasting reliability by 18% with ensemble methods. Delivered predictive outputs directly into Power BI dashboards, aligning analytics with executive KPIs and operational decisions. Cut ETL preparation time by 35% by creating SQL preprocessing pipelines.
ML Engineer at Mphasis
August 1, 2021 - August 1, 2021
Lowered loan misclassification by deploying Gradient Boosting and Random Forest models on structured financial datasets. Heightened campaign conversion rates by 12% through K-Means clustering for segmentation of large-scale customer transaction data. Improved preprocessing efficiency by automating feature engineering workflows with SQL and Pandas pipelines. Shortened deployment cycles by containerizing models with Docker and establishing automated CI/CD delivery pipelines. Increased predictive accuracy through systematic feature selection, cross-validation, and parameter optimization. Enhanced credit scoring pipelines supporting ingestion of structured and semi-structured datasets at scale. Reduced manual data checks by automating preprocessing validation.

Education

Master of Science in Information Systems at Saint Louis University, St Louis, MO, USA
August 1, 2023 - May 1, 2025
Master of Science in Information Systems at Saint Louis University, St Louis, MO, USA
August 1, 2023 - May 1, 2025

Qualifications

Add your qualifications or awards here.

Industry Experience

Financial Services, Software & Internet, Telecommunications, Professional Services

Skills

Experience Level