Available to hire
SUMMARY Highly motivated Software Engineer specializing in AI/ML systems and data infrastructure, with a Master’s degree (expected May 2025). Proven experience coding in Python, C++and SQL, adept at designing and deploying solutions leveraging LLMs, RAG and Embeddings within Kubernetes/Cloud environments. Eager to contribute to Microsoft’s live service operations by focusing on code quality, deployment best practices and ensuring system reliability at scale.
Work Experience
AI/ML Engineer at Uber / R K Software Services LLC
June 1, 2025 - PresentFine-tuned large language models with Hugging Face, improving prediction accuracy by 30% and reducing task completion time by 40%, enabling enterprise clients to scale production NLP workloads. Deployed CNN and Transformer-based computer vision models on AWS SageMaker using Docker and FastAPI, achieving 98% classification accuracy and reducing inference latency by 25% across real-time image recognition systems. Automated CI/CD pipelines with GitHub Actions, Docker, and Kubernetes for ML workflows, improving deployment reliability by 30% and reducing release cycle times by 35%. Designed real-time monitoring and alerting frameworks for ML models, reducing production failures by 20% and ensuring reliable performance in mission-critical client-facing applications. Integrated bias detection algorithms and fairness metrics, reducing model bias by 20% and improving AI ethics compliance. Implemented privacy-preserving AI techniques and encryption strategies, strengthening data security and ach
AI/ML Engineer at Intello Labs
April 1, 2020 - March 1, 2023Designed predictive models using regression, clustering, and decision trees in scikit-learn, increasing forecasting accuracy by 35% and enabling clients to optimize retail inventory and financial demand planning decisions. Built RNN and Transformer architectures in PyTorch for time-series forecasting, reducing forecasting errors by 22% and delivering highly accurate demand predictions for global retail and supply chain clients. Engineered ETL pipelines with SQL, Pandas, and NumPy, reducing data preparation time by 45%, improving dataset quality, and accelerating downstream model training workflows across multiple business units. Standardized feature engineering processes, improving data consistency and boosting model performance by 18%. Delivered Tableau and Power BI dashboards integrating predictive outputs, reducing reporting cycles by 30% and enabling executives to make data-driven decisions faster. Automated experimentation workflows with Azure ML and Google Colab, cutting experime
Education
Master of Science in Data Science at University of Memphis
January 11, 2030 - May 1, 2025Qualifications
Industry Experience
Software & Internet, Professional Services, Healthcare, Financial Services, Retail
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