Poojhaa Chandrasekaran
I’m an AI/ML Engineer with 3+ years of experience building predictive analytics and Generative AI solutions, with a focus on turning complex data into reliable, production-ready systems.
About
I’m an AI/ML Engineer with 3+ years of experience building predictive analytics and Generative AI solutions, with a focus on turning complex data into reliable, production-ready systems. I work primarily with Python and SQL, and I’ve delivered end-to-end ML pipelines and data transformations across investment/financial analytics and technology environments using AWS, Snowflake, scikit-learn, and PyTorch.
I enjoy designing trustworthy AI experiences—especially retrieval-augmented generation (RAG) with embeddings and vector search—so outputs are grounded in enterprise data. I’ve also implemented model validation, monitoring-minded practices, hallucination mitigation checks, and responsible-AI considerations, while collaborating across engineering and data science teams to automate recurring workflows and improve decision-making for stakeholders.
- 3 years' experience
- Joined September 2026
Industry experience
- Financial Services
- Software & Internet
Work experience
Engineered Python and SQL data pipelines on AWS and Snowflake to transform structured investment datasets into validated, model-ready features, processing 1M+ financial records across recurring analytical workflows. Developed predictive modeling and classification solutions using scikit-learn and PyTorch with feature engineering, evaluation, and error analysis to improve reliability for financial analytics stakeholders. Prototyped enterprise Generative AI workflows using LLM prompting, retrieval, and structured query generation to help business users interact with complex datasets via natural-language interfaces. Implemented retrieval workflows with embeddings and vector search to ground AI responses against enterprise data, adding validation and hallucination-mitigation checks. Operationalized ML workflows via AWS-based pipelines and Git-driven development practices, supporting multiple AI/ML use cases and embedding data validation and responsible-AI considerations into the delivery p
Built Python and SQL data-processing workflows for business analytics, cleaning and transforming operational datasets into analysis-ready tables that supported reporting and predictive analytics across multiple software projects. Developed predictive analytics models using Python with feature engineering, train-test validation, and performance evaluation to identify patterns and generate actionable business predictions. Created real-time analytics workflows using Python and PostgreSQL to organize transactional and operational data for interactive dashboards. Deployed analytics workloads on AWS to support scalable processing and reporting, integrating cloud-based data workflows with application/analytics components. Automated recurring data preparation and reporting tasks with Python and SQL, reducing manual effort and ensuring consistent access to refreshed datasets and outputs. Collaborated with software engineers, QA, and stakeholders during Agile cycles to translate analytical requi
Education
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