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I’m an AI/ML and Generative AI engineer with 9+ years of experience building production-grade AI systems for domains like financial services and healthcare. My work spans end-to-end pipelines—from exploratory data analysis and feature engineering to scalable model training, deployment, and monitoring—often powering near-real-time decisioning. I specialize in LLM/RAG solutions and agentic workflows, leveraging tools like LangChain/LangGraph and Model Context Protocol patterns to securely connect models with enterprise APIs, vector search, and knowledge bases. I also build strong MLOps/LLMOps foundations using Kubernetes, MLflow, CI/CD, and observability to ensure reliability, explainability, and governance across real-world environments.…I’m an AI/ML and Generative AI engineer with 9+ years of experience building production-grade AI systems for domains like financial services and healthcare. My work spans end-to-end pipelines—from exploratory data analysis and feature engineering to scalable model training, deployment, and monitoring—often powering near-real-time decisioning. I specialize in LLM/RAG solutions and agentic workflows, leveraging tools like LangChain/LangGraph and Model Context Protocol patterns to securely connect models with enterprise APIs, vector search, and knowledge bases. I also build strong MLOps/LLMOps foundations using Kubernetes, MLflow, CI/CD, and observability to ensure reliability, explainability, and governance across real-world environments.

Lakshmi Poojitha Vangapalli

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

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I’m an AI/ML and Generative AI engineer with 9+ years of experience building production-grade AI systems for domains like financial services and healthcare. My work spans end-to-end pipelines—from exploratory data analysis and feature engineering to scalable model training, deployment, and monitoring—often powering near-real-time decisioning. I specialize in LLM/RAG solutions and agentic workflows, leveraging tools like LangChain/LangGraph and Model Context Protocol patterns to securely connect models with enterprise APIs, vector search, and knowledge bases. I also build strong MLOps/LLMOps foundations using Kubernetes, MLflow, CI/CD, and observability to ensure reliability, explainability, and governance across real-world environments.…I’m an AI/ML and Generative AI engineer with 9+ years of experience building production-grade AI systems for domains like financial services and healthcare. My work spans end-to-end pipelines—from exploratory data analysis and feature engineering to scalable model training, deployment, and monitoring—often powering near-real-time decisioning. I specialize in LLM/RAG solutions and agentic workflows, leveraging tools like LangChain/LangGraph and Model Context Protocol patterns to securely connect models with enterprise APIs, vector search, and knowledge bases. I also build strong MLOps/LLMOps foundations using Kubernetes, MLflow, CI/CD, and observability to ensure reliability, explainability, and governance across real-world environments.

Available to hire

I’m an AI/ML and Generative AI engineer with 9+ years of experience building production-grade AI systems for domains like financial services and healthcare. My work spans end-to-end pipelines—from exploratory data analysis and feature engineering to scalable model training, deployment, and monitoring—often powering near-real-time decisioning.

I specialize in LLM/RAG solutions and agentic workflows, leveraging tools like LangChain/LangGraph and Model Context Protocol patterns to securely connect models with enterprise APIs, vector search, and knowledge bases. I also build strong MLOps/LLMOps foundations using Kubernetes, MLflow, CI/CD, and observability to ensure reliability, explainability, and governance across real-world environments.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Senior AI/ML & Generative AI Engineer at JPMorgan Chase

December 1, 2023 - Present

Partnered with fraud, AML, and compliance teams to deliver production fraud detection and investigation platforms. Engineered near-real-time scoring of millions of transactions using supervised ML, deep learning, and LLM-based retrieval to support compliant fraud decisioning. Integrated card transactions, ACH/wire transfers, customer profiles, device metadata, and historical fraud labels across AWS data platforms with consistent schema enforcement. Built a hybrid GraphRAG architecture (FAISS, Elasticsearch, Neo4j) for contextual document and graph reasoning, enabling grounded LLM responses. Designed secure multi-agent orchestration with LangGraph, MCP, and tool interfaces to securely interact with enterprise APIs, databases, and knowledge bases. Deployed GPU-enabled inference services on EKS with Triton, implemented CI/CD and governance, and established monitoring dashboards for performance, latency, drift, and data quality. Exposed services via FastAPI/Node.js microservices for real-t

AI/ML Engineer at Premier Inc.

April 1, 2022 - June 1, 2023

Developed predictive models to identify patient deterioration and hospital readmission risks using structured EHR data. Built HIPAA-compliant AI solutions on Azure ML and AKS with automated training, monitoring, and security controls to support clinical decision-making workflows. Pulled vitals, labs, diagnoses, medications, and claims into a unified analytics layer; engineered features; built models with XGBoost and LightGBM; calibrated and tested stability. Built PyTorch-based sequence models for time-series signals; developed NLP pipelines to extract symptoms and clinical indicators; applied transformer-based models to summarize clinical narratives and surface supporting evidence. Designed retrieval pipelines using Azure AI Search, vector embeddings, and similarity search over approved clinical guidelines to improve contextual recommendations. Deployed low-latency inference endpoints on Azure ML/Triton, containerized workloads on AKS, and implemented RBAC and Key Vault for HIPAA-alig

Machine Learning Engineer at Edward Jones

January 1, 2020 - March 1, 2022

Built ML-driven analytics to help financial advisors identify client churn risk and analyze portfolio behavior. Developed predictive models (XGBoost, LightGBM, Random Forest) for churn and portfolio risk scoring, including calibration and stability testing. Built time-series forecasting models (LSTM, ARIMA) for asset trend analysis and advisor reporting. Created semantic search and document retrieval systems using sentence-transformer embeddings and FAISS for research reports and advisor notes. Implemented conversational interfaces with Dialogflow CX and integrated ML predictions into advisor-facing dashboards. Leveraged Vertex AI Pipelines for automated model training, validation, deployment, monitoring, and retraining; containerized inference services on GKE; established monitoring for drift and performance; incorporated SHAP explainability for audit and review.

Role: Data Scientist at Luxoft India Pvt. Ltd.

July 1, 2018 - August 31, 2019

Developed NLP and ML solutions for customer analytics, including text classification, topic modeling, and demand forecasting. Built NLP pipelines using spaCy and NLTK for entity extraction and normalization, and trained LightGBM classifiers with TF-IDF/Word2Vec features. Implemented LDA topic modeling and exposed batch inference through Flask APIs on AWS EC2. Created time-series forecasting models using ARIMA and Prophet, engineered behavioral features from clickstream/transaction data, and applied Bayesian optimization for hyperparameter tuning. Containerized services with Docker and implemented CI/CD with Git-based workflows.

Data Scientist at Luxoft India Pvt. Ltd.

July 1, 2018 - August 1, 2019

Developed NLP and ML solutions for customer analytics, including text classification, topic modeling, and demand forecasting. Built NLP pipelines with spaCy and NLTK; used LightGBM for multilingual review classification; implemented topic modeling (LDA) and batch inference via Flask APIs on AWS EC2. Developed time-series forecasting with ARIMA and Prophet; integrated clickstream and transactional data to engineer behavioral features for XGBoost-based models. Containerized Flask services and established CI/CD with Git workflows.

Python Developer (Data Validation / ETL) at PwC

March 1, 2016 - June 30, 2018

Developed Python-based ETL pipelines and backend services for financial data validation, ensuring data quality and audit readiness. Processed and normalized JSON API data with Pandas, handled schema inconsistencies with validation, and optimized NumPy computations for statistical/time-series analysis. Built Flask services integrating Isolation Forest and scikit-learn anomaly detection for transaction validation workflows. Added unit/integration testing with PyTest, followed TDD and peer code reviews, implemented logging/monitoring, enforced JSON schema validation, and automated Git-based CI workflows.

Python Developer at PwC

March 1, 2016 - June 1, 2018

Developed Python-based ETL pipelines for financial data validation, ensuring data quality and audit readiness. Processed and normalized JSON API data with Pandas; optimized NumPy computations for time-series analysis; built Flask-based services integrating Isolation Forest and statistical anomaly detection. Implemented JSON schema validation, automated Git-based CI workflows, and robust logging/monitoring for production reliability.