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I am a senior AI/ML Engineer with 9+ years of experience designing, developing, and deploying AI/ML and Generative AI solutions across finance, healthcare, and enterprise domains. I specialize in scalable architectures, real-time inference, and explainable AI, with a proven focus on compliance, security, and operational excellence. I am proficient in Python, SQL, and Bash for data processing and real-time ETL pipelines, and I work with TensorFlow, PyTorch, and multimodal AI to build robust, responsible AI solutions.…I am a senior AI/ML Engineer with 9+ years of experience designing, developing, and deploying AI/ML and Generative AI solutions across finance, healthcare, and enterprise domains. I specialize in scalable architectures, real-time inference, and explainable AI, with a proven focus on compliance, security, and operational excellence. I am proficient in Python, SQL, and Bash for data processing and real-time ETL pipelines, and I work with TensorFlow, PyTorch, and multimodal AI to build robust, responsible AI solutions.

Lakshmi Poojitha Vangapalli

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

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I am a senior AI/ML Engineer with 9+ years of experience designing, developing, and deploying AI/ML and Generative AI solutions across finance, healthcare, and enterprise domains. I specialize in scalable architectures, real-time inference, and explainable AI, with a proven focus on compliance, security, and operational excellence. I am proficient in Python, SQL, and Bash for data processing and real-time ETL pipelines, and I work with TensorFlow, PyTorch, and multimodal AI to build robust, responsible AI solutions.…I am a senior AI/ML Engineer with 9+ years of experience designing, developing, and deploying AI/ML and Generative AI solutions across finance, healthcare, and enterprise domains. I specialize in scalable architectures, real-time inference, and explainable AI, with a proven focus on compliance, security, and operational excellence. I am proficient in Python, SQL, and Bash for data processing and real-time ETL pipelines, and I work with TensorFlow, PyTorch, and multimodal AI to build robust, responsible AI solutions.

Available to hire

I am a senior AI/ML Engineer with 9+ years of experience designing, developing, and deploying AI/ML and Generative AI solutions across finance, healthcare, and enterprise domains. I specialize in scalable architectures, real-time inference, and explainable AI, with a proven focus on compliance, security, and operational excellence. I am proficient in Python, SQL, and Bash for data processing and real-time ETL pipelines, and I work with TensorFlow, PyTorch, and multimodal AI to build robust, responsible AI solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Senior AI/ML Engineer at JPMorgan Chase & Co.

January 1, 2025 - November 5, 2025

Led the design and deployment of an enterprise AI/ML platform for transaction fraud and anomaly detection. Implemented LangChain-based RAG pipelines integrated with Azure OpenAI and Pinecone to contextualize millions of transactions, reducing false-positive alerts by 32%. Enabled multi-agent fraud reasoning with LangGraph and AutoGen, and indexed 15 TB of embeddings using FAISS on Azure ML to achieve sub-second anomaly clustering. Fine-tuned domain-specific classifiers with LLM Guardrails to mitigate prompt injection, and deployed inference at scale (8K+ requests/sec) on AKS using vLLM, DeepSpeed, and Triton. Built explainable inference workflows with SHAP/LIME and monitored pipelines with Prometheus/Grafana; automated retraining with Airflow; ensured SOX/GDPR compliance via Key Vault and OAuth2. Versioned artifacts with DVC and integrated with Azure DevOps for end-to-end lineage.

AI/ML Engineer at Edward Jones

December 1, 2024 - December 1, 2024

Developed scalable clinical AI/ML workflows on AWS SageMaker using PyTorch and XGBoost to predict disease onset and readmission risk by fusing structured EHR, lab, and wearable data stored in S3. Built multimodal models combining radiology image embeddings with physician notes to support diagnostic decisions. Automated ML pipelines via SageMaker Pipelines and Airflow with feature drift detection and retraining triggers; created a medical virtual assistant (FastAPI/Lambda) to summarize risk factors and suggest clinical trials. Applied anomaly detection on insurance claims and deployed models through Triton Inference Server on EKS with Terraform-managed GPU clusters; ensured HIPAA compliance and audit-ready documentation.

Machine Learning Engineer at Edward Jones

July 1, 2023 - July 1, 2023

Engineered an enterprise ML platform powering portfolio risk prediction, churn forecasting, and advisor recommendations. Implemented end-to-end MLOps with MLflow, DVC, and GitHub Actions on AKS, enabling reproducible experiments and controlled deployments. Developed high-performance inference services using KServe, Triton, and TorchServe with blue-green deployments; built RAG systems using FAISS and Weaviate for semantic retrieval over 8M+ market research documents. Delivered multimodal pipelines (text, tabular, and visuals) for anomaly detection and cross-asset correlation; established observability with Evidently AI, Prometheus, and Azure Monitor; integrated SHAP/LIME for explainability and regulatory readiness.

Data Scientist at PwC

October 1, 2020 - October 1, 2020

Built NLP and ML solutions including NER models with spaCy, LightGBM ensembles on TF-IDF/Word2Vec, and topic modeling with LDA. Deployed real-time inference via Flask APIs on AWS EC2; implemented data ingestion from S3, in addition to MySQL integration for cross-sell optimization. Implemented time-series with ARIMA/Prophet for demand trends; automated CI/CD pipelines and containerized services with Docker; optimized data processing, validated schemas with tests, and improved inference latency through feature engineering.

Python Developer at PwC

June 1, 2018 - June 1, 2018

Developed Python ETL pipelines consolidating multi-source financial data for reconciliation across 10+ regional systems. Built Flask microservices hosting outlier detection models for transactional validation; implemented unit tests with pytest; improved processing efficiency via NumPy optimizations; ensured data integrity with schema validation and robust logging.