M.S. in Computer Science | Specializing in ML Infrastructure & Production ML Systems
Building production-grade ML infrastructure with cloud-native technologies. Expertise in model deployment automation, scalable inference systems, and ML pipeline orchestration using AWS, Terraform, and Python. Bridging the gap between Data Science and Production Engineering.
I am an MLOps Engineer and AWS Certified Solutions Architect with a strong foundation in both Machine Learning and Production Infrastructure. Currently pursuing M.S. in Computer Science at Fairleigh Dickinson University, I specialize in building end-to-end ML systems that seamlessly integrate model development, deployment automation, and production monitoring.
My expertise centers on ML infrastructure engineering: designing serverless inference APIs with auto-scaling (AWS Lambda, DynamoDB), orchestrating distributed training jobs with fault tolerance (Go-based job engines), and implementing reproducible ML environments using Infrastructure as Code (Terraform). I've architected systems handling high-concurrency model serving with sub-millisecond latency and built resilient job queues optimized for large-scale ML workloads.
With professional experience at HiTrust Inc. developing production microservices and optimizing data pipelines (30% performance improvement), plus hands-on ML work at Astra Technology (time-series forecasting, Computer Vision PoCs), I bring a unique combination of ML understanding and infrastructure expertise. I'm passionate about creating robust, scalable systems that empower data scientists to deploy models confidently in production.
MLOps infrastructure, ML model serving platforms, and distributed ML training systems
MLOps-ready serverless platform architected for deploying and serving ML models at scale. Built event-driven inference APIs using AWS Lambda with auto-scaling for variable load, DynamoDB for low-latency prediction logging, and comprehensive monitoring. 100% Infrastructure as Code with Terraform managing isolated Dev/Staging/Prod environments. Designed to handle high-concurrency model predictions with sub-100ms latency.
Fault-tolerant job orchestration system engineered in Go for managing large-scale ML training workloads and distributed inference pipelines. Leverages Goroutines and Channels for concurrent job execution with thread safety, critical for parallelized hyperparameter tuning and batch predictions. Implements Write-Ahead Log (WAL) for crash recovery, ensuring long-running training jobs survive node failures. Ideal for orchestrating multi-hour model training across distributed clusters.
Complete ML lifecycle automation from training to production deployment. Building automated pipeline with data versioning (DVC), experiment tracking (MLflow), model registry with versioning, and CI/CD for models. Features automated model testing, A/B deployment strategies, and drift detection. Deploys models as FastAPI endpoints on AWS Lambda with Terraform IaC. Includes comprehensive monitoring dashboards for model performance metrics, prediction latency, and data distribution shifts.
MLOps Engineering β’ ML Infrastructure β’ Model Deployment & Monitoring β’ Cloud Architecture
Building scalable backend systems and cloud infrastructure
β’ Developed secure microservices for a financial transaction system handling millions of requests, ensuring high availability (HA) for critical live services
β’ Optimized database queries and backend logic, accelerating report generation by 30% for data-heavy workloads
β’ Dockerized applications and orchestrated deployments on Kubernetes, managing resource allocation and scaling strategies for production environments
β’ Utilized Python (Pandas) to process internal datasets and build time-series prediction models to forecast user behavior patterns
β’ Collaborated with engineering teams to launch a Proof of Concept (PoC) for an AI-driven Computer Vision project (NTT Japan)
β’ Defined technical requirements for ML model deployment and integration with production systems
M.S. in Applied Computer Science
Relevant Coursework:
β’ Artificial Intelligence (Python)
β’ Advanced Topics in Operating Systems (Go)
β’ Systems Programming (C)
Big Data Analytics Bootcamp
Intensive training in data analytics, machine learning, and big data technologies.
Hands-on projects with real-world datasets and ML model development.
Actively seeking MLOps Engineer and ML Infrastructure roles in Vancouver, BC | Open to remote opportunities