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I'm Khaleef Mohammed, an AI/ML Engineer with 4+ years of experience delivering production-grade ML and generative AI solutions across enterprise and regulated environments. I thrive at building end-to-end ML systems—from data engineering, model development, and deployment to monitoring and governance—translating complex ML outputs into measurable business impact. I excel at collaborating with cross-functional teams to improve model accuracy, reduce deployment cycles, and optimize cloud costs while upholding compliance and responsible AI practices. I’m passionate about scalable MLOps, model monitoring, and turning data into strategic decisions.…I'm Khaleef Mohammed, an AI/ML Engineer with 4+ years of experience delivering production-grade ML and generative AI solutions across enterprise and regulated environments. I thrive at building end-to-end ML systems—from data engineering, model development, and deployment to monitoring and governance—translating complex ML outputs into measurable business impact. I excel at collaborating with cross-functional teams to improve model accuracy, reduce deployment cycles, and optimize cloud costs while upholding compliance and responsible AI practices. I’m passionate about scalable MLOps, model monitoring, and turning data into strategic decisions.

Khaleef Mohammed

Data Scientist, AI Engineer, App Developer, +5

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I'm Khaleef Mohammed, an AI/ML Engineer with 4+ years of experience delivering production-grade ML and generative AI solutions across enterprise and regulated environments. I thrive at building end-to-end ML systems—from data engineering, model development, and deployment to monitoring and governance—translating complex ML outputs into measurable business impact. I excel at collaborating with cross-functional teams to improve model accuracy, reduce deployment cycles, and optimize cloud costs while upholding compliance and responsible AI practices. I’m passionate about scalable MLOps, model monitoring, and turning data into strategic decisions.…I'm Khaleef Mohammed, an AI/ML Engineer with 4+ years of experience delivering production-grade ML and generative AI solutions across enterprise and regulated environments. I thrive at building end-to-end ML systems—from data engineering, model development, and deployment to monitoring and governance—translating complex ML outputs into measurable business impact. I excel at collaborating with cross-functional teams to improve model accuracy, reduce deployment cycles, and optimize cloud costs while upholding compliance and responsible AI practices. I’m passionate about scalable MLOps, model monitoring, and turning data into strategic decisions.

Available to hire

I’m Khaleef Mohammed, an AI/ML Engineer with 4+ years of experience delivering production-grade ML and generative AI solutions across enterprise and regulated environments. I thrive at building end-to-end ML systems—from data engineering, model development, and deployment to monitoring and governance—translating complex ML outputs into measurable business impact.

I excel at collaborating with cross-functional teams to improve model accuracy, reduce deployment cycles, and optimize cloud costs while upholding compliance and responsible AI practices. I’m passionate about scalable MLOps, model monitoring, and turning data into strategic decisions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Machine Learning Engineer at Jack Henry & Associates

January 1, 2025 - Present

Delivered production-grade ML solutions in Python and TensorFlow to strengthen digital banking fraud detection. Built and deployed classification models achieving a 12% reduction in fraud losses, improving accuracy and reducing false positives across core digital banking systems. Implemented Airflow and Docker-based retraining pipelines to automate model lifecycle, increasing deployment reliability and reducing manual interventions. Deployed Hugging Face NLP models to enhance personalized recommendations and customer engagement within banking applications. Optimized experiments using AWS SageMaker and Databricks, enabling scalable training, distributed processing, and hyperparameter tuning while lowering costs by 22%. Integrated ML APIs to banking platforms for improved risk evaluation and regulatory compliance. Designed Tableau dashboards to monitor ROC, AUC, precision, and recall in real time.

Machine Learning Engineer at Infosys

August 1, 2023 - October 15, 2025

Built supervised and unsupervised ML models to improve predictive accuracy by 20% across healthcare, retail, and BFSI clients. Developed scalable data pipelines with Pandas, NumPy, and PySpark to streamline cleaning, transformation, and feature scaling for enterprise datasets, reducing preprocessing time by 30%. Automated retraining workflows with Airflow and MLflow to improve reproducibility and efficiency. Deployed Flask and FastAPI APIs to enable real-time predictions, supporting data-driven enterprise decisions and lowering inference latency. Collaborated in Agile teams, maintained Git repositories, and aligned ML deliverables with business requirements to shorten delivery timelines by 25%. Implemented monitoring for deployed models including ROC, AUC, precision, recall, and drift detection to sustain long-term accuracy.

Machine Learning Engineer at Jack Henry & Associates

January 1, 2025 - October 27, 2025

Decreased fraud losses by delivering classification models in Python and TensorFlow, improving accuracy and reducing false positives in core digital banking fraud-detection systems. Increased workflow efficiency by implementing Airflow and Docker to automate retraining pipelines, enhance deployment reliability, and eliminate recurring manual interventions. Boosted product adoption by deploying Hugging Face NLP models to enable personalized recommendations and support customer engagement strategies within banking applications. Lowered experimentation costs by leveraging AWS SageMaker and Databricks for scalable training, distributed processing, and hyperparameter tuning across multiple ML use cases. Enhanced regulatory compliance by integrating ML APIs into banking platforms, improving risk evaluation and reducing operational errors. Accelerated reporting speed by designing Tableau dashboards monitoring ROC, AUC, precision, and recall in real time.

Machine Learning Engineer at Infosys

August 1, 2023 - August 1, 2023

Improved predictive accuracy by building supervised and unsupervised ML models for healthcare, retail, and BFSI clients. Reduced preprocessing time by developing scalable data pipelines with Pandas, NumPy, and PySpark. Cut retraining cycles by automating workflows with Airflow and MLflow, ensuring reproducibility and efficiency for deployed ML solutions. Lowered inference latency by deploying Flask and FastAPI APIs for real-time predictions. Supported agile delivery by collaborating in cross-functional teams, maintaining Git repositories, and aligning ML deliverables with business requirements. Sustained long-term accuracy by monitoring ROC, AUC, precision, recall, and drift, retraining proactively to prevent performance degradation.

AI/ML Engineer at Jack Henry & Associates

January 1, 2025 - Present

Designed, trained, and deployed production-grade ML models using Python, TensorFlow, and PyTorch, improving transaction anomaly detection accuracy by 18% across high-volume financial systems. Developed and finetuned Transformer-based NLP pipelines using Hugging Face to automate customer query classification, reducing manual triage effort by 25% and improving response SLAs across multiple channels. Built end-to-end MLOps workflows with Docker, Flask, FastAPI, and CI/CD pipelines, cutting model deployment cycles from 2 weeks to 2 days while ensuring reproducibility, version control, and rollback safety. Managed and optimized AWS SageMaker and Azure ML environments, improving training efficiency and reducing cloud resource waste while maintaining strict data security and compliance standards. Implemented model monitoring, drift detection, and automated retraining strategies, reducing production incidents and maintaining consistent model performance in client-facing applications. Conducted

AI/ML Engineer at Infosys

June 1, 2020 - August 1, 2023

Developed and deployed predictive ML models for finance and customer analytics, improving forecasting accuracy by 12% while supporting enterprise-scale decision-making. Built CNN-based computer vision models for document image classification, reducing manual verification time by 30% across regulated workflows. Automated data preprocessing and feature engineering pipelines using Pandas and NumPy, reducing preparation time by 40% and improving data consistency across projects. Integrated ML models into internal platforms via Flask REST APIs, enabling real-time inference and reliable operational deployment. Applied model interpretability, bias mitigation, and fairness techniques, supporting responsible AI initiatives and compliance with internal audit requirements. Monitored production ML systems, maintaining 95% uptime, retraining models as needed and delivering performance reports with actionable insights to stakeholders. Partnered with cross-functional teams to define data requirements