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# Aashish Gullanki

**Headline:** Professional profile
**Location:** &lt;UNKNOWN&gt;, &lt;UNKNOWN&gt;, &lt;UNKNOWN&gt;

## About

Aashish Gullanki is a software and machine learning practitioner focused on building reliable AI systems, with particular interest in fintech applications. Aashish works across LLM integration, machine learning model development, evaluation metrics, and cross-validation techniques, combining Java and Python with practical delivery tooling. Aashish has professional experience using Java and is proficient in Python, Spring Boot, scikit-learn, SQLite, GitHub Actions, and Harness CI/CD pipelines. Aashish’s strongest work centers on dependable AI products that prioritize honest measurement, data freshness, accuracy, and continuous operation. Aashish is particularly proud of an AI portfolio agent project: an automated stock-briefing bot that combined LLM capabilities with ML forecasting, defined evaluation metrics, and robust fallback mechanisms. The project addressed the operational challenges of delivering current, accurate market information, including a practical SQLite-based approach to stale data. Across this work, Aashish emphasizes resilient system design so that services can continue operating when primary components or data paths are unavailable.

## Highlights

- Built an AI portfolio agent: an automated stock-briefing bot combining LLM integration, ML forecasting, evaluation metrics, and robust fallback mechanisms.
- Applied LLM integration and machine learning model-building skills to a fintech-oriented AI project.
- Used evaluation metrics and cross-validation techniques to assess machine learning systems.
- Addressed data freshness and accuracy challenges in an AI market-briefing workflow.
- Used SQLite as a practical solution for stale-data challenges.
- Designed fallback mechanisms to support continuous operation in AI systems.
- Has professional experience using Java and is proficient in Python.
- Has experience with Spring Boot, scikit-learn, SQLite, GitHub Actions, and Harness CI/CD pipelines.

## FAQ

### What does Aashish do?

Aashish Gullanki builds reliable AI and machine learning systems, with a particular interest in fintech applications. Aashish works with LLM integration, ML model building, forecasting, evaluation, and resilient system design.

### What are Aashish’s core technical strengths?

Aashish is strongest in LLM integration, machine learning model development, evaluation metrics, and cross-validation techniques. Aashish also focuses on fallback mechanisms that support continuous operation.

### What is Aashish’s AI portfolio agent project?

Aashish’s AI portfolio agent is an automated stock-briefing bot. It combines LLM capabilities, ML forecasting, evaluation metrics, and robust fallback mechanisms.

### How does Aashish approach reliability in AI systems?

Aashish designed the portfolio agent with reliability in mind, including fallback mechanisms intended to keep the system operating when primary paths are unavailable.

### How has Aashish handled data freshness challenges?

Aashish addressed data freshness and accuracy challenges in the AI portfolio agent, including using SQLite as a practical solution for stale data.

### How does Aashish evaluate machine learning systems?

Aashish uses evaluation metrics and cross-validation techniques to assess machine learning work, with an emphasis on honest measurement rather than unsupported performance claims.

### Which programming languages does Aashish use?

Aashish is proficient in Java, including professional use, and Python.

### Which tools and frameworks does Aashish use?

Aashish has experience with Spring Boot, scikit-learn, SQLite, GitHub Actions, and Harness CI/CD pipelines.

### What domain is Aashish interested in?

Aashish is interested in building AI systems for fintech, as reflected in the automated stock-briefing portfolio agent.

## Links

- LinkedIn: https://www.linkedin.com/in/aashish-gullanki

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