Applied ML & Data Engineer focused on building, evaluating, and operating production-grade AI systems from large-scale data pipelines and model evaluation to cloud deployment, monitoring, and iteration under real-world constraints.
I’m a Master’s student in Applied Machine Intelligence at Northeastern University. I build end-to-end ML systems that bridge the gap between research prototypes and reliable production deployments. My work spans applied machine learning, data engineering, and MLOps, with a bias toward measurable impact and clear evaluation.
Currently interested in: Applied ML / ML Engineering, Data Engineering (ML-heavy), MLOps / ML Platform, AI Systems.
class CoreTechnicalFocus:
def __init__(self):
self.applied_ml_evaluation = ["Feature Engineering","Baselines","Metrics","Calibration","Error Analysis"]
self.data_engineering = ["Apache Spark","Databricks","SQL","ETL Pipelines","Batch Processing"]
self.mlops_cloud = ["Docker","CI/CD","Model Deployment","Monitoring","AWS","Google Cloud Platform"]
self.llm_systems = ["RAG Pipelines","Retrieval & Grounding","Citation-backed Q&A"]
def summary(self):
print("Building reliable, production-grade ML systems end-to-end.")
focus = CoreTechnicalFocus()
focus.summary()
- Advanced ML Model Deployment Pipelines - Building scalable and automated MLOps workflows
_421--9431-green?style=for-the-badge&logo=phone&logoColor=white){kind=icon}
⭐️ From yashasrn33