Harshil Patil
Ai Engineer @Teradata
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WORK HISTORY
Ai Engineer @Teradata
ON, CA
Built and deployed end-to-end machine learning models for classification, regression, and recommendation systems using Scikit-learn, TensorFlow, and XGBoost within an Agile/Scrum workflow.• Designed and maintained Python-based microservices using FastAPI to serve real-time predictions via REST APIs, supporting millions of daily requests in production.• Automated model retraining workflows and data ingestion pipelines using Apache Airflow, reducing manual efforts and enabling continuous model updates from raw data sources.• Collaborated with data engineers to design cloud-based architecture (AWS S3, EC2, SageMaker) for scalable model training and deployment, supporting large-scale ETL operations using PySpark.• Implemented model performance tracking and drift detection using MLflow, custom logging, and real-time dashboards built in Streamlit and Grafana.• Applied NLP techniques such as BERT embeddings, entity recognition, and sentiment analysis to extract insights from unstructured text in product feedback and customer support data.• Optimized deep learning model inference by converting models into ONNX and TensorRT formats for GPU deployment, reducing latency by 40% in live environments.• Conducted regular code reviews, unit testing (Pytest), and CI/CD automation using GitHub Actions to ensure model stability and reproducibility across development environments.• Worked cross-functionally with backend engineers, data analysts, and DevOps to standardize model deployment processes and improve observability.
EDUCATION
Conestoga College
Postgraduate Degree, Web Development
J. H. Ambani Saraswati Vidymandir School
Higher Secondary Education , Science
Alard Charitable Trusts Alard College of Engineering & Management,Pune
BE - Bachelor of Engineering, Computer Engineering
ABOUT HARSHIL PATIL
AI/ML Engineer with 3+ years of experience in developing scalable machine learning systems, integrating AI models into production APIs, automating data pipelines, and working with large datasets in cloud-based environments. Proficient in Python, SQL, TensorFlow, and MLOps practices. Strong foundation in supervised/unsupervised learning, deep learning, model monitoring, data wrangling, and cross-functional collaboration with engineering, analytics, and product teams. Adept at building reusable components, accelerating deployment cycles, and translating business needs into measurable ML outcomes.
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