I am an AI & Data Engineer with more than six years of engineering experience and a strong background in AWS cloud and backend engineering. At TymeX, I build data platforms and internal AI agents, and lead a team of six engineers.
My foundation is backend engineering: production microservices, APIs and integrations, with AWS cloud experience. I bring that foundation into data platforms for digital banking and AI systems. I care about reliable data, clear ownership and services people can depend on. My recent work focuses on AI agents, context engineering and the controls needed to operate them responsibly.
EXPERIENCE
My engineering journey.
Aug 2022 — present
TymeXSenior Data Engineer
Build data platforms for digital banking and internal AI agents for engineering operations. Lead a team of six engineers, connecting platform architecture, reliable delivery and practical AI adoption.
Applied AI & agent workflows
Designed internal agents for log triage and pipeline error diagnosis, integrated into the team’s existing engineering workflows.
Connected tools through MCP and managed curated instructions, persistent memory and context budgets. The operational agents saved roughly four hours of routine work per engineer per week.
Agent evaluation & LLMOps
Established tracing, output evaluation, CI quality gates and cost controls to support reliable agent delivery.
Data mesh & self-service platforms
Built a data mesh platform on Databricks, Spark and Delta Lake, with domain-owned data products and self-service platform capabilities.
Connected domain ownership with shared platform standards, metadata, lineage and access control.
Data ingestion & pipelines
Worked across batch and streaming pipelines as part of the wider data platform, alongside downstream feature-store delivery.
Data contracts & publication
Built versioned YAML contracts covering data structure, semantics and quality expectations.
Added mechanical validation at publication so producers and consumers could work against shared, versioned definitions; advanced platform work from proof of concept to adopted internal standards.
Data quality & governance
Integrated automated data-quality checks with policy as code, metadata, lineage and access control.
Connected quality expectations in contracts with validation at the data-product publication boundary.
Features for machine learning
Built the lending feature store end to end: offline pipelines on Databricks Feature Store and online serving through DynamoDB, sharing feature definitions across training and live decisions.
Technical leadership & delivery
Led six engineers across planning, code review, mentoring and onboarding, alongside hands-on platform and agent engineering.
Built production backend services and APIs for healthcare products and media operations, working with AWS and containerized services. This foundation supports my later data-platform and AI engineering.
Production services & integration
Developed microservices and APIs for healthcare and media workflows, including partner integration through API Gateway.
Worked with Python, PostgreSQL, Kafka, Docker, Kubernetes and AWS.
Quality & auditability
Contributed test automation and audit tracking to production software delivery.
EDUCATION / 2016 — 2020VNUHCM — University of Information Technology
EXPERTISE
Where I can contribute.
AI agents & reliability
Agents with tool use, memory, tracing, and output evaluation before deployment.
MCPRAGEvaluationLLMOps
Data platforms
Batch and streaming ingestion, domain-owned data products and self-service lakehouse platforms, alongside ML feature serving.
DatabricksSparkDelta LakeKafkaAWS
Data quality & governance
Versioned data contracts, automated quality checks, policy as code, lineage, access control and shared engineering standards.
Unity CatalogGreat ExpectationsSodaData contracts
AWS cloud
Deep AWS cloud expertise grounded in backend systems and data platforms, including API Gateway integrations and DynamoDB online feature serving.
AWSAPI GatewayDynamoDB
Backend engineering
Production microservices, APIs and partner integrations, with test automation and auditability.
PythonPostgreSQLKafkaDockerKubernetes
Technical leadership
Hands-on engineering alongside team planning, code review, mentoring and onboarding.
Team leadershipArchitectureCode reviewMentoring
RESEARCH DIRECTION
Autonomy we can verify.
My intended doctoral research direction is Applied and Agentic AI: a platform-level control plane for autonomous systems. I want to extend contracts, policy, lineage, and evaluation so that autonomy can be graded, checked before deployment, and audited afterwards.