Shahitya Khan Shammo

Shahitya Khan Shammo

AI Engineer & Full-Stack Developer

AI/ML Integration
End to End Application Development
FinTech Solutions

About Me

I am a Computer Science graduate from Hong Kong Baptist University (CGPA 3.72/4.00) with an exchange semester at the Technical University of Munich.

I am interested in leveraging various AI tools to build real-world innovative applications that can help address problems for end-users across various different domains. My focus is on creating intuitive tools that transform complex information into clear, functional experiences.

Currently specializing in designing multi-agent AI pipelines that leverage LLMs, graph databases and tools to carry out complex research and provide actionable insights in the finance sector. To further bridge the gap between technical execution and specialized industry knowledge, I am currently studying for the CFA Level I exam (which I will be taking in August of 2026).

With a key focus on application development and deployment, agentic AI and data engineering, I am passionate about coming up with solutions that make a difference for the person behind the screen.

What I Bring

Full-stack development expertise
AI/ML integration & LLM pipelines
Performance optimization
Cloud deployment & DevOps
AI native application development
Modern UI/UX design principles

Skills & Technologies

Programming Languages

PythonJavaScriptTypeScriptJava

Frameworks & Libraries

ReactNext.jsVue.jsNode.jsPyTorch

Databases & Tools

PostgreSQLMongoDBNeo4jpgvectorApache AirflowDockerGitLinux

Cloud Services

GCP Cloud RunCloud ComposerCloudSQLCompute Engine

AI Tools

LLMs / AI AgentsAgentic WorkflowsBuilding AI Tools

Featured Work

A showcase of my recent work, from concept to deployment.

FinCatch

University Projects

Work Experience

Key Contributions

  • Built real-time financial data analysis pipelines on Apache Airflow, integrating LLMs to generate actionable insights from news, earnings releases, transcripts, and filings.
  • Leveraged AI agents and a Neo4j graph database to improve research quality; created tools for agents to query PostgreSQL, search the web, and traverse the graph DB.
  • Reduced vector-search latency by 5× using HNSW indexes on pgvector.
  • Designed pipelines to extract and standardise management guidance from earnings releases and transcripts, enabling guidance-vs-actuals tracking.
  • Built interactive dashboard features for market event analyses, trend analyses, investment theses, and management guidance track records (React, Next.js, TypeScript).
  • Leveraged GCP tooling: Cloud Composer, Cloud Run, and Cloud SQL Studio.
PythonApache AirflowLLMsNeo4jPostgreSQLpgvectorGCPReactNext.jsTypeScript

Contact Me

How I prefer to work?

Fast turnaround
Efficient development with modern tools and best practices
Quality assured
Production-ready code with comprehensive testing
Clear communication
Regular updates and transparent project management