Mary-Julia D.

I’m an AWS Cloud & MLOps Engineer with 4+ years of experience building production-ready, scalable, and cost-efficient cloud infrastructures. I specialize in designing end to end ML pipelines, deploying serverless AI/ML applications, and automating complex workflows using AWS services. I also have extensive experience breaking down end-to-end data pipelines, setting up deterministic test criteria, and analyzing failure modes across high-stakes systems. Adept at translating messy operational parameters into unambiguous, objective grading rubrics and synthetic test suites. Known for a detail-obsessed approach to verification-evaluating math, logic, and operational edge cases against zero-margin-for-error standards. Bringing a practical, ground-truth mindset to benchmarking industrial, automated, and algorithmic outputs. My framework and skills are as follows: • AWS Expertise: EC2, S3, Lambda, DynamoDB, RDS, IAM, API Gateway, SageMaker, CloudFormation, CDK, ECS/EKS. • MLOps & AI Infrastructure: Build, train, and deploy ML models with SageMaker; S3-driven feature pipelines; serverless inference endpoints; Glue ETL; notebook-driven experimentation. • Automation & DevOps: CI/CD with CodePipeline, GitHub Actions, Jenkins; IaC with Terraform, CDK, CloudFormation; monitoring with CloudWatch and CloudTrail. • Process & Quality Control: Root-Cause Analysis, Failure Mode & Effects Analysis (FMEA), Boundary Testing, Deterministic Rubric Design, Synthetic Dataset Generation. • Systems & Workflow Architecture: End-to-End Data Pipelines, Automated Workflow Execution, Distributed Systems Logic, State Machine Validation. • Technical Rigor & Verification: Statistical Validation, Output Quality Auditing, Edge-Case Identification, Mathematical Logic Checks, Operational Risk Modeling. What I can do for you: • Deploy end-to-end ML pipelines quickly and reliably. • Build serverless AI/ML applications with real-time inference endpoints. • Automate cloud deployments and DevOps workflows for high availability and cost optimization. • Design and execute objective evaluation suites and rubric systems to test the reliability, output consistency, and failure modes of automated data pipelines and Al logic models. • Perform deep-dive quality audits on complex technical deliverables, catching subtle mathematical errors, flawed baseline assumptions, and logic breakdowns prior to production deployment. If you need either Cloud or Systems engineer who can turn ML expertise into deployable, production ready solutions fast, let’s connect. I’m ready to get your project running smoothly today.