Self-Assessment

During my internship at AWS First Cloud AI Journey from 08/09/2025 to 09/12/2025, I had the opportunity to learn, practice, and apply the knowledge acquired in school to a real-world working environment.
I participated in AIVanguard Team as the role of Fullstack Developer, through which I improved my skills in Frontend&Backend Programming (especially on NextJS and Typescript), AWS Well-Architecture Framework, Git, GitLab CI/CD, Serverless, Corporate Culture and Growth Mindset….

In terms of work ethic, I always strived to complete tasks well, complied with workplace regulations, and actively engaged with colleagues to improve work efficiency.

To objectively reflect on my internship period, I would like to evaluate myself based on the following criteria:

No.CriteriaDescriptionGoodFairAverage
1Professional knowledge & skillsBuilt Project 1, used Serverless Framework, S3, Lambda, DynamoDB, Bedrock,…
2Ability to learnRapidly adopted new tools (Amazon Q, Bedrock, CloudThinker, Serverless)
3ProactivenessInitiated tasks: architecture designs, hackathon proposal, demos
4Sense of responsibilityMet deadlines, delivered slides, demos, and final deployment
5DisciplineGenerally on time; room to improve strict adherence to schedules
6Progressive mindsetActively sought feedback and iterated on Project 1 and documentation
7CommunicationPresenting ideas and reporting work clearly
8TeamworkCollaborated on GameDay and hackathon; communicated with mentors
9Professional conductRespectful and proactive in team interactions
10Problem-solving skillsSolved many integration issues; seek further practice on complex debugging
11Contribution to project/teamMajor contributor to Project 1 features and architecture
12OverallConsistent progress from basics to production-ready deployment

Needs Improvement

  • Strengthen discipline and strictly comply with the rules and regulations of the company or any organization
  • Improve problem-solving thinking
  • Enhance communication skills in both daily interactions and professional contexts, including handling situations effectively

I primarily worked on Project 1 (a cloud-native application integrating front-end, serverless back-end, real-time features, and GenAI components). Over 14 weeks I progressed from foundational AWS knowledge (Week 1) to designing and deploying the production system (Weeks 12–14). Worklog highlights include:

  • Weeks 1–3: AWS fundamentals (EC2, S3, IAM), AWS CLI, S3 static hosting, Amplify, Cognito, and initial MVP work.
  • Weeks 4–7: Serverless patterns (Lambda, API Gateway), RDS/DynamoDB basics, architecture design, and VPBank hackathon proposal (Digital Charity with Blockchain&AI).
  • Weeks 8–11: Well-Architected review, CI/CD & GitLab pipelines, GenAI and Bedrock topics, perimeter/security workshops, and GameDay participation.
  • Weeks 12–14: Implemented backend functions, serverless WebSocket, integrated API Bedrock, deployed Project 1 to production, added CloudFront/WAF/Route53, and finalized demos and documentation.

Key measurable outcomes:

  • Delivered a working Project 1 with frontend + serverless backend and local/production Serverless Framework configuration.
  • Implemented real-time features (WebSocket) and integrated API Bedrock for AI functionality.
  • Deployed to AWS production with monitoring (CloudWatch), edge protection (CloudFront/WAF), and stricter IAM policies.
  • Authored event recaps and week-by-week worklog entries; attended 10+ AWS events and workshops.

Although the internship produced several measurable outcomes, I rated some core competencies as Fair (see table above):

  • Professional knowledge & skills (No.1) and Ability to learn (No.2) — I can implement many services and patterns, but I need deeper theoretical grounding and a more structured learning plan to increase reliability and speed when encountering unfamiliar problems.
  • Communication (No.7) — I can produce event recaps and documentation, yet some reports are still verbose or lack concise executive summaries; verbal presentation pacing needs work.
  • Professional conduct (No.9) — generally respectful and collaborative, but there were a few moments of inconsistency in following some team conventions; this should be tightened.

Areas for improvement

  • Deepen foundational knowledge and systematic learning: schedule deliberate study slots for core AWS concepts (Well-Architected, networking, security) and follow structured courses.
  • Improve learning velocity: adopt a note-taking + spaced-repetition routine and keep a learning journal to convert event learnings into actionable tasks.
  • Communication: produce concise, one-page technical summaries and executive bullets for each major deliverable; practice short demo presentations to improve pacing.
  • Professional conduct & discipline: adopt stricter timeboxing, early status updates, and a personal checklist for code review and documentation standards.
  • Advanced debugging & design patterns: schedule focused debugging sessions, use tracing and load-testing, and review postmortems for lessons learned.

Action plan (next 3 months)

  1. Maintain a weekly checklist and use GitLab issues to plan sprints and track progress (sprint-based timeboxing).
  2. Schedule focused debugging sessions: use tracing (X-Ray / CloudWatch traces) and load testing to find bottlenecks.
  3. Write two concise one-page technical summaries per month (architecture + decisions) and present to mentor for feedback.
  4. Complete at least one advanced AWS certification course module (Well-Architected or DevOps specialty) to strengthen theoretical foundations.

Closing remark

This internship moved me from foundational AWS concepts to designing, building, and deploying a production-capable project that integrates serverless architecture and GenAI features. While I made solid progress, the Fair ratings for Professional knowledge, Ability to learn, Communication, and Professional conduct highlight areas where I must focus next. I am committed to the action plan above and motivated to close these gaps over the coming months.