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I am Shamala Chandrappa, an AI and data engineer based in San Jose, CA, with 5 years of hands-on experience building scalable ML-powered data platforms and secure AI applications. I focus on end-to-end pipelines, model deployment, and risk-aware AI for healthcare, finance, and enterprise software. I enjoy collaborating across teams, evaluating new technologies, and delivering tangible outcomes—like real-time fraud detection, HIPAA-compliant data workflows, and interpretable AI for clinical decision support. I’m open to remote roles and opportunities to apply responsible AI at scale.…I am Shamala Chandrappa, an AI and data engineer based in San Jose, CA, with 5 years of hands-on experience building scalable ML-powered data platforms and secure AI applications. I focus on end-to-end pipelines, model deployment, and risk-aware AI for healthcare, finance, and enterprise software. I enjoy collaborating across teams, evaluating new technologies, and delivering tangible outcomes—like real-time fraud detection, HIPAA-compliant data workflows, and interpretable AI for clinical decision support. I’m open to remote roles and opportunities to apply responsible AI at scale.

Shamala Chandrappa San Jose

AI Engineer, Data Scientist, Full Stack Developer, +2

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I am Shamala Chandrappa, an AI and data engineer based in San Jose, CA, with 5 years of hands-on experience building scalable ML-powered data platforms and secure AI applications. I focus on end-to-end pipelines, model deployment, and risk-aware AI for healthcare, finance, and enterprise software. I enjoy collaborating across teams, evaluating new technologies, and delivering tangible outcomes—like real-time fraud detection, HIPAA-compliant data workflows, and interpretable AI for clinical decision support. I’m open to remote roles and opportunities to apply responsible AI at scale.…I am Shamala Chandrappa, an AI and data engineer based in San Jose, CA, with 5 years of hands-on experience building scalable ML-powered data platforms and secure AI applications. I focus on end-to-end pipelines, model deployment, and risk-aware AI for healthcare, finance, and enterprise software. I enjoy collaborating across teams, evaluating new technologies, and delivering tangible outcomes—like real-time fraud detection, HIPAA-compliant data workflows, and interpretable AI for clinical decision support. I’m open to remote roles and opportunities to apply responsible AI at scale.

Available to hire

I am Shamala Chandrappa, an AI and data engineer based in San Jose, CA, with 5 years of hands-on experience building scalable ML-powered data platforms and secure AI applications. I focus on end-to-end pipelines, model deployment, and risk-aware AI for healthcare, finance, and enterprise software. I enjoy collaborating across teams, evaluating new technologies, and delivering tangible outcomes—like real-time fraud detection, HIPAA-compliant data workflows, and interpretable AI for clinical decision support. I’m open to remote roles and opportunities to apply responsible AI at scale.

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Language

English

Fluent

Work Experience

AI Engineer - AI & LLM Safety at San Jose State University Research Foundation

January 1, 2025 - Present

Collaborated with Dr. Simon Shim and Dr. Kichan g Lee on research applying Generative AI and predictive ML to health care data, focusing on developing safe and interpretable models for clinical decision support and risk prediction in cardiac surgery patients. Built multi-modal workflows using EH R datasets with ~1,700 proteins and biomarkers measured at 24h, 36h, and 72h pre- and post-surgery, extracting physiologic signatures and SHAP-based risk factors to inform ICU stay length, preoperative medications, and inflammatory markers. Developed responsive UI components with React.js to enable clinicians to interact with outputs; maintained HIPAA-aligned data pipelines and audit logs to ensure privacy and reproducibility across institutions. Improved decision-making efficiency and auditability in HIPAA-regulated environments.

Data & AI Engineer at PeopleLens.ai

June 1, 2024 - August 31, 2024

Delivered data-driven insights to support GTM and Revenue Operations, enabling data-backed decision making for campaigns. Built streaming and batch pipelines using GCP Dataflow, Pub/Sub, and Composer to consolidate data from 10+ sources (Salesforce, Google Calendar, HubSpot) into BigQuery and Snowflake. Improved data accessibility by 70% and reduced ad-hoc data requests by 50% through a well-defined lakehouse model. Implemented PII-aware ELT validation and schema enforcement, ensuring GDPR compliance and consistent cross-team data. Led regression analyses to forecast campaign performance and conversions, reducing forecast errors.

Senior Software Engineer - Development, ML & Data Platforms at FIS Global

July 1, 2019 - July 1, 2023

Led migration of a legacy on-prem payment system to AWS, building a multi-region, fault-tolerant architecture using AWS Global Accelerator, Route 53, and ALB. Redesigned payment workflows in Java/Spring Boot and integrated PostgreSQL and Redis for transaction processing. Automated infrastructure provisioning with Terraform and CloudFormation; established CI/CD pipelines and release processes. Implemented detailed logging, metrics, and alerting with CloudWatch and Grafana for end-to-end observability. Reduced manual interventions during outages by 60% and improved reconciliation accuracy, building a scalable foundation capable of handling 10M+ transactions per day.

AI Engineer - AI & LLM Safety at San José State University Research Foundation

January 1, 2025 - Present

Collaborated with SJSU and Harvard Medical School on research applying generative AI and predictive ML to healthcare data, focusing on developing safe, interpretable models for clinical decision support and risk prediction in cardiac surgery. Built end-to-end AI/ML pipelines, including data integration from multi-source EHR data, model training with PyTorch and Scikit-Learn, and secure RESTful APIs with data governance and auditability.

Data and AI Engineer at PeopleLens.ai

June 1, 2024 - August 1, 2024

Delivered data-driven insights to support go-to-market and revenue operations. Built GCP-based data pipelines (Dataflow, Pub/Sub, Composer) consolidating 10+ sources into BigQuery and Snowflake; improved data accessibility by 70% and reduced ad-hoc requests by half. Developed DAGs, implemented PII-aware ELT validation, and set up real-time monitoring with CloudWatch and Grafana.