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I am a Senior GenAI / AI Agentic Engineer with 9+ years of experience delivering intelligent systems across healthcare, pharma, supply chain, and financial services in India and the United States. I design autonomous agentic workflows and multi-agent systems, building robust RAG pipelines, memory-enabled reasoning, and tool-based orchestration for enterprise environments. I specialize in deploying scalable AI microservices on AWS, Azure, and GCP, with careful attention to governance, compliance, and explainability. I collaborate closely with prompt engineers, risk/compliance teams, and data experts to create auditable, grounded AI that supports decision-makers in regulated settings.…I am a Senior GenAI / AI Agentic Engineer with 9+ years of experience delivering intelligent systems across healthcare, pharma, supply chain, and financial services in India and the United States. I design autonomous agentic workflows and multi-agent systems, building robust RAG pipelines, memory-enabled reasoning, and tool-based orchestration for enterprise environments. I specialize in deploying scalable AI microservices on AWS, Azure, and GCP, with careful attention to governance, compliance, and explainability. I collaborate closely with prompt engineers, risk/compliance teams, and data experts to create auditable, grounded AI that supports decision-makers in regulated settings.

Siri Patlolla

AI Engineer, Data Scientist, Full Stack Developer, +4

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I am a Senior GenAI / AI Agentic Engineer with 9+ years of experience delivering intelligent systems across healthcare, pharma, supply chain, and financial services in India and the United States. I design autonomous agentic workflows and multi-agent systems, building robust RAG pipelines, memory-enabled reasoning, and tool-based orchestration for enterprise environments. I specialize in deploying scalable AI microservices on AWS, Azure, and GCP, with careful attention to governance, compliance, and explainability. I collaborate closely with prompt engineers, risk/compliance teams, and data experts to create auditable, grounded AI that supports decision-makers in regulated settings.…I am a Senior GenAI / AI Agentic Engineer with 9+ years of experience delivering intelligent systems across healthcare, pharma, supply chain, and financial services in India and the United States. I design autonomous agentic workflows and multi-agent systems, building robust RAG pipelines, memory-enabled reasoning, and tool-based orchestration for enterprise environments. I specialize in deploying scalable AI microservices on AWS, Azure, and GCP, with careful attention to governance, compliance, and explainability. I collaborate closely with prompt engineers, risk/compliance teams, and data experts to create auditable, grounded AI that supports decision-makers in regulated settings.

Available to hire

I am a Senior GenAI / AI Agentic Engineer with 9+ years of experience delivering intelligent systems across healthcare, pharma, supply chain, and financial services in India and the United States. I design autonomous agentic workflows and multi-agent systems, building robust RAG pipelines, memory-enabled reasoning, and tool-based orchestration for enterprise environments.

I specialize in deploying scalable AI microservices on AWS, Azure, and GCP, with careful attention to governance, compliance, and explainability. I collaborate closely with prompt engineers, risk/compliance teams, and data experts to create auditable, grounded AI that supports decision-makers in regulated settings.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Gen AI Engineer at Deutsche Bank

December 1, 2024 - Present

Working in a central AI / Risk team to design GenAI and Retrieval-Augmented Generation (RAG) solutions for compliance, risk, and regulatory analysts. Grounding LLM outputs on internal policies, regulatory texts, and historical case documents to ensure traceability, auditability, and governance. Built ingestion pipelines to pull documents into Azure Data Lake with chunking, metadata tagging, and text normalization; used Azure Databricks (PySpark) to process large corpora and prepare features for ML and RAG workflows; integrated with Snowflake to join structured risk data with document metadata; implemented vector retrieval with metadata filters; designed prompts to keep outputs concise and source-grounded; tracked experiments with MLflow; exposed results via Power BI; collaborated with risk, compliance, and model validation teams for governance and security.

Senior ML Engineer (NLP) at Cardinal Health

January 1, 2022 - August 1, 2023

Part of a supply chain analytics team building ML and NLP solutions for demand forecasting, anomaly detection, and operational insights. Built forecasting models using Python, Scikit-learn, XGBoost, and LightGBM on historical order, shipment, and inventory data. Designed PySpark / Spark pipelines on Databricks (AWS) to process large volumes of data; implemented data ingestion with AWS Glue; developed NLP pipelines (spaCy/NLTK) to process free-text notes and incident descriptions; created embedding-based similarity lookups for past incidents; applied early RAG-style retrieval by combining retrieved context with current features; built batch inference jobs on AWS EC2 / Databricks; published outputs to Power BI/Tableau dashboards.

Data Scientist (ML Engineer) at Merck

December 1, 2019 - December 1, 2021

Built ML and NLP workflows over structured R&D data and unstructured clinical/regulatory documents to support scientists and regulatory staff. Developed document classification, similarity grouping, and basic risk tagging using spaCy / NLTK; used AWS S3 as the central data lake and PySpark / Spark for large-scale text processing. Trained Scikit-learn models to categorize documents and flag items for review, implemented basic TF‑IDF / embedding-based similarity search, and collaborated with regulatory and clinical SMEs to ensure outputs aligned with workflows. Integrated results into Tableau dashboards and documented labeling schemes and model logic for validation and audit purposes.

Data Scientist at Merck

December 1, 2019 - December 1, 2021

Worked with research / regulatory analytics to build ML and NLP workflows over structured R&D data and clinical/regulatory documents. Developed ML pipelines for document classification, similarity grouping, and tagging; NLP workflows for cleaning and normalization; leveraged AWS S3 as central data lake; used PySpark / EMR / Databricks to process large text corpora; trained Scikit-learn models; implemented TF-IDF / embedding-based similarity search; collaborated with regulatory and clinical SMEs; integrated results into Tableau dashboards; documented modeling decisions and data lineage for validation.

Data Scientist at CitiusTech

January 1, 2018 - September 1, 2019

Healthcare analytics (Payer / Provider) focusing on data prep, SQL, Python, and feature engineering to support risk and utilization modeling; performed EDA to identify drivers and outliers; contributed to dashboarding and reporting; documented data sources, feature definitions, and modeling steps for auditability.

Junior Data Scientist at GAVS Technologies

March 1, 2016 - December 1, 2017

Supported analytics projects for healthcare and enterprise clients by pulling data from Oracle/relational systems, performing data cleaning and validation, and delivering Excel-friendly extracts. Assisted with basic dashboards in Tableau and created simple analytics reports; documented data sources and report logic to enable repeatable analytics tasks.

Data Scientist at GAVS Technologies

March 1, 2016 - December 1, 2017

Supported healthcare analytics projects for payer/provider clients; performed data cleaning, transformation, and feature engineering; assisted with Scikit-learn models for risk and utilization; conducted EDA; built Tableau dashboards; documented data prep logic and model behavior; collaborated with domain SMEs to validate outputs and ensure reproducibility.