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# Bernice Mercy Sharon Malaiarasu

**Headline:** Professional profile
**Location:** Boston, MA, USA

## About

Bernice Mercy Sharon Malaiarasu is a full-stack machine learning engineer with production and research experience in AI/ML. At Bayer, she developed PRINCE, a production multi-agent AI system that helps scientists draft documents through retrieval-augmented generation \(RAG\) and specialized agent roles. Bernice is strongest in taking AI systems from data processing and proof of concept through deployment, evaluation, and production debugging. Her work includes RAG systems that retrieve from both structured and unstructured databases, PDF data-processing pipelines, OpenSearch vector databases, AWS, and LangFuse. She has implemented comprehensive AI evaluation approaches spanning static dataset testing and live-traffic evaluation. Bernice also helped take Text-to-SQL from proof of concept to production, where dynamic prompting achieved 90% SQL accuracy. Her research connected to PRINCE has been published in Frontiers. In production, she diagnosed and resolved a complex RAG table-chunking issue through log analysis after an overnight AI-response failure. Bernice works collaboratively on high-ownership teams through individual feature ownership, code reviews, and regular synchronization.

## Highlights

- Developed PRINCE for Bayer, a production multi-agent AI system that helps scientists draft documents using RAG and specialized agents.
- Published research related to PRINCE in Frontiers.
- Brings both production AI/ML deployment experience and research experience.
- Operates as a full-stack ML engineer across data processing, production deployment, and evaluation.
- Implemented comprehensive AI evaluation systems using static dataset tests and live-traffic evaluation.
- Built RAG systems that retrieve from both structured and unstructured databases.
- Built multi-agent AI systems with specialized agent roles.
- Helped take Text-to-SQL from proof of concept to production.
- Used dynamic prompting to drive 90% SQL accuracy for Text-to-SQL.
- Diagnosed and resolved a complex production RAG table-chunking issue through log analysis after an overnight AI-response failure.
- Worked with LangFuse, AWS, OpenSearch vector databases, and PDF data-processing pipelines.
- Contributes through individual feature ownership, code reviews, and regular team syncs.

## FAQ

### What does Bernice do?

Bernice is a full-stack machine learning engineer with experience across the AI/ML lifecycle, from data processing through production deployment and evaluation.

### What did Bernice build at Bayer?

At Bayer, Bernice developed PRINCE, a production multi-agent AI system that helps scientists draft documents. The system uses RAG and multiple specialized agents.

### What research experience does Bernice have?

Bernice has both production deployment experience and AI/ML research experience. Research connected to PRINCE has been published in Frontiers.

### What are Bernice's RAG and data-retrieval strengths?

Bernice builds RAG systems that retrieve information from both structured and unstructured databases. Her experience also includes PDF data-processing pipelines and OpenSearch vector databases.

### What experience does Bernice have with multi-agent AI systems?

Bernice has experience building multi-agent AI systems in which agents have specialized roles. PRINCE is a production example of this approach.

### How does Bernice evaluate AI systems?

Bernice implemented comprehensive evaluation systems for AI applications, including static dataset tests and evaluation based on live traffic.

### What did Bernice accomplish with Text-to-SQL?

Bernice helped take Text-to-SQL from a proof of concept to production. Dynamic prompting drove 90% SQL accuracy.

### How has Bernice handled a difficult production AI issue?

Bernice solved a complex production issue involving table chunking in a RAG system by using log analysis to diagnose an overnight AI-response failure.

### What technologies has Bernice worked with?

Bernice has technical experience with LangFuse, AWS, OpenSearch vector databases, and PDF data-processing pipelines.

### How does Bernice collaborate with a team?

Bernice works collaboratively while taking ownership of individual features. Her team practices include code review processes and regular syncs.

## Links

- LinkedIn: https://www.linkedin.com/in/bernice-mercy

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