Machine Learning Engineer with Biostatistics background specializing in end-to-end ML pipelines, production-grade deployments, and MLOps automation. I bridge the gap between statistical rigor and scalable ML systems, transforming healthcare and business data into actionable insights through robust, production-ready solutions.
🔹 Core Expertise: Deep Learning • MLOps • Cloud Infrastructure (GCP/AWS) • Statistical Modeling • Healthcare Analytics
🔹 Engineering Focus: CI/CD Pipelines • Docker/Kubernetes • Model Monitoring • Automated Workflows
🔹 Domain Knowledge: Biostatistics • Time Series Forecasting • Predictive Analytics • Customer Intelligence
Role: ML/DS Engineer & MLOps Specialist Specialization: - Production ML Systems Architecture - Healthcare Data Analytics & Biostatistics - Cloud-Native ML Deployments (GCP, AWS) - Automated ML Pipeline Development Technical Stack: Frameworks: TensorFlow, Keras, PyTorch, Scikit-learn MLOps: MLflow, DVC, Airflow, Jenkins, GitHub Actions Cloud: GCP (GKE, Cloud Run, Artifact Registry), AWS (ECS, ECR) Containerization: Docker, Kubernetes Backend: FastAPI, Flask Databases: PostgreSQL, MongoDB Monitoring: Prometheus, Grafana, Evidently AI
End-to-end MLOps pipeline with FastAPI, Docker, DVC & MLflow for banking churn prediction
Impact: 92% accuracy • 85% latency reduction • Real-time API predictions
Automated ML lifecycle with Jenkins CI/CD and GCP cloud integration
Impact: Deployment time reduced from days to hours
Kubernetes-orchestrated ML serving with GitHub Actions CI/CD on Google Kubernetes Engine
Impact: Handles 10K+ requests/min with auto-scaling
DVC + GitHub Actions pipeline deploying to GCP Cloud Run with Artifact Registry
Impact: 60% cost reduction through serverless architecture
Production-ready Apache Airflow DAGs with Docker containerization
Impact: 99.9% uptime with automated error recovery
Time series prediction using Simple RNN for NSE multivariate stock data
Impact: RMSE < 0.02 with strong trend prediction
Power BI dashboard analyzing 450K+ patient records for wait-time optimization
Impact: 23% reduction in average wait times
Biostatistics & Healthcare Analytics
- Clinical trial data analysis and statistical modeling
- Healthcare operational analytics and patient flow optimization
- Survival analysis and longitudinal data modeling
- Epidemiological data processing and insights generation
Machine Learning Engineering
- Deep learning architecture design and optimization
- Time series forecasting and predictive analytics
- Natural Language Processing (NLP) applications
- Computer vision and image analysis
MLOps & Production Systems
- End-to-end ML pipeline automation
- Model versioning, monitoring, and drift detection
- CI/CD for ML systems with comprehensive testing
- Cloud-native architecture design and implementation
✅ Automated ML Pipelines: Reduced model deployment time by 80% through comprehensive MLOps automation
✅ Production Scalability: Built systems serving 10K+ predictions/minute with 99.9% uptime
✅ Cost Optimization: Achieved 60% infrastructure cost reduction using serverless architectures
✅ Healthcare Impact: Improved patient wait-time analytics affecting 450K+ patient records
✅ Cross-Platform Expertise: Successfully deployed ML models across GCP, AWS, and on-premise infrastructure
🔄 Currently exploring: LLMOps, Federated Learning, ML Model Interpretability
📖 Reading: Advanced MLOps practices, Cloud-native ML architectures
🎯 Next Goals: Kubernetes certification (CKA), Advanced GCP ML certifications
🎯 Statistical Rigor: Biostatistics background ensures robust experimental design and model validation
⚙️ Production Mindset: Every project built with scalability, monitoring, and maintainability in focus
🔄 Full-Stack MLOps: From data ingestion to model monitoring - comprehensive pipeline expertise
☁️ Cloud-Native: Deep experience with GCP and AWS for building resilient ML infrastructure
📈 Business Impact: Always connecting technical solutions to measurable business outcomes
I'm always interested in discussing ML/DS opportunities, MLOps best practices, or collaboration on impactful projects.
📧 Email: stat.data247@gmail.com 💼 LinkedIn: Connect with me 🌐 Portfolio: View my work