Open to opportunities

Syed Asfar

@syedasfar

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AI/ML Engineer with 8 years of experience in intelligent systems.

United States

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What I'm looking for

I seek a role that fosters innovation and collaboration, allowing me to leverage my AI expertise to drive impactful solutions.

I am an AI/ML Engineer with over 8 years of hands-on experience in developing, deploying, and optimizing intelligent systems across various industries including FinTech, Healthcare, and E-commerce. My expertise lies in building scalable machine learning pipelines, training deep learning models, and deploying real-time AI services using advanced technologies such as Python, TensorFlow, and PyTorch.

Throughout my career, I have successfully created production-ready AI products that address complex business challenges and deliver measurable value. I have a proven track record in NLP, computer vision, and MLOps workflows, and I thrive in agile environments, collaborating with cross-functional teams to ensure reproducibility, experimentation, and performance tuning in all projects I undertake.

Experience

Work history, roles, and key accomplishments

Current

AI/ML Engineer

Current

HTD Health

Jun 2024 - Present (1 year 1 month)

Built and deployed a real-time fraud detection ML system using XGBoost and autoencoder anomaly detection pipelines, integrated via FastAPI and Kafka. Led implementation of model versioning and deployment pipelines using MLflow, Docker, and GitHub Actions for CI/CD.

XGBoost fastAPI Kafka MLFlow Docker GitHub Actions)BERT Sagemaker Transformers

Machine Learning Engineer

Congniteq

Feb 2023 - Present (2 years 5 months)

Built a deep learning system using LSTM networks to predict patient deterioration from time-series vitals, deployed as a clinical decision support tool. Created custom image classification models to identify skin lesion categories from medical images using CNNs and ResNet.

PyTorch Keras OpenCV Flask GCP ResNet SHAP Docker

Junior ML Engineer

Codebridge

Aug 2021 - Present (3 years 11 months)

Developed a recommendation system using collaborative filtering and matrix factorization (SVD, ALS), integrated into the website and email campaigns. Built time-series forecasting models using LSTM and GRU networks to predict sales across 12 product categories.