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AI & ML Engineer with 5 years of enterprise experience across FinTech and Healthcare, specializing in Generative AI, deep learning, and predictive analytics. I build production-ready, compliant AI/ML systems that combine NLP, computer vision, and time-series forecasting with scalable data engineering. I architect end-to-end MLOps/LLMOps pipelines using Python and PyTorch/TensorFlow, and deliver high-performance solutions across hybrid cloud environments (Azure/GCP). I focus on responsible AI using model governance and interpretability (SHAP/LIME), with robust monitoring to ensure reliability in regulated settings.…AI & ML Engineer with 5 years of enterprise experience across FinTech and Healthcare, specializing in Generative AI, deep learning, and predictive analytics. I build production-ready, compliant AI/ML systems that combine NLP, computer vision, and time-series forecasting with scalable data engineering. I architect end-to-end MLOps/LLMOps pipelines using Python and PyTorch/TensorFlow, and deliver high-performance solutions across hybrid cloud environments (Azure/GCP). I focus on responsible AI using model governance and interpretability (SHAP/LIME), with robust monitoring to ensure reliability in regulated settings.

Swarna Reddy

AI Engineer, Data Scientist, Full Stack Developer, +3

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AI & ML Engineer with 5 years of enterprise experience across FinTech and Healthcare, specializing in Generative AI, deep learning, and predictive analytics. I build production-ready, compliant AI/ML systems that combine NLP, computer vision, and time-series forecasting with scalable data engineering. I architect end-to-end MLOps/LLMOps pipelines using Python and PyTorch/TensorFlow, and deliver high-performance solutions across hybrid cloud environments (Azure/GCP). I focus on responsible AI using model governance and interpretability (SHAP/LIME), with robust monitoring to ensure reliability in regulated settings.…AI & ML Engineer with 5 years of enterprise experience across FinTech and Healthcare, specializing in Generative AI, deep learning, and predictive analytics. I build production-ready, compliant AI/ML systems that combine NLP, computer vision, and time-series forecasting with scalable data engineering. I architect end-to-end MLOps/LLMOps pipelines using Python and PyTorch/TensorFlow, and deliver high-performance solutions across hybrid cloud environments (Azure/GCP). I focus on responsible AI using model governance and interpretability (SHAP/LIME), with robust monitoring to ensure reliability in regulated settings.

Available to hire

AI & ML Engineer with 5 years of enterprise experience across FinTech and Healthcare, specializing in Generative AI, deep learning, and predictive analytics. I build production-ready, compliant AI/ML systems that combine NLP, computer vision, and time-series forecasting with scalable data engineering.

I architect end-to-end MLOps/LLMOps pipelines using Python and PyTorch/TensorFlow, and deliver high-performance solutions across hybrid cloud environments (Azure/GCP). I focus on responsible AI using model governance and interpretability (SHAP/LIME), with robust monitoring to ensure reliability in regulated settings.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI & ML Engineer at Goldman Sachs, TX, USA

August 1, 2024 - Present

Designed and deployed a scalable enterprise AI platform integrating Generative AI, ML, NLP, and big data engineering for investment research, risk analytics, regulatory compliance, and operational forecasting across asset servicing workflows. Built production-grade RAG systems using LangChain/LlamaIndex with Azure OpenAI and developed conversational copilots to enable natural language interaction with financial data and internal systems. Implemented end-to-end ML pipelines for risk classification, anomaly detection, and time-series forecasting using Python, PyTorch, and Scikit-learn, improving early risk detection and forecast accuracy. Engineered TB-scale big data pipelines using PySpark/Databricks/Spark/Delta Lake, and built FastAPI REST services to support AI applications. Implemented LLMOps/MLOps frameworks with Azure ML, Docker, and CI/CD (including monitoring with Azure Monitor/Application Insights), enabling reproducible deployments and drift detection. Applied Responsible AI an

AI/ML Engineer at Goldman Sachs

August 1, 2024 - Present

Led design and deployment of a scalable enterprise AI platform integrating Generative AI, ML, NLP and big data engineering for investment research, risk analytics, regulatory compliance and operational forecasting. Built production-grade LangChain/LlamaIndex-based RAG pipelines and scalable APIs on Azure OpenAI, reducing research time by 40%. Developed end-to-end ML pipelines for risk classification, anomaly detection and forecasting, improving early risk detection accuracy by 27%. Implemented conversational AI copilots and integrated Bloomberg and Refinitiv APIs for real-time data workflows. Engineered high-performance big data pipelines with PySpark, Azure Databricks, Apache Spark and Delta Lake, processing TB-scale datasets. Built NLP/document intelligence models to extract entities, obligations and compliance risks from contracts and filings with 38% improvement in accuracy. Implemented MLOps and LLMOps using Azure ML, Git, Docker and CI/CD, achieving faster deployment cycles and a

AI & ML Engineer at Accenture – India

June 1, 2020 - July 1, 2023

Architected and scaled machine learning and generative AI solutions across Apollo Hospitals’ digital network to support clinical decision-making, disease risk prediction, and hospital resource optimization while maintaining HIPAA/NABH compliance. Built real-time ingestion pipelines using Apache Kafka and Airflow to synchronize clinical events (HL7 interfaces) and enable readmission forecasting. Used PySpark on GCP and BigQuery for large-scale processing. Developed supervised ML models (TensorFlow/PyTorch) for cardiovascular disease risk using feature engineering on FHIR-compliant EMR data. Built deep learning computer vision models (CNNs/RNNs, Keras/MONAI) for detecting pulmonary anomalies in medical imagery. Implemented LLM-based clinical NLP using Hugging Face Transformers for extracting diagnostic insights from physician notes. Applied evaluation and interpretability using scikit-learn metrics, XGBoost ensembles, SHAP/LIME (including R workflows) to reduce bias. Deployed inference

AI & ML Engineer at Accenture

June 1, 2020 - July 31, 2023

Architected real-time data ingestion pipelines using Apache Kafka and Airflow to synchronize clinical events via HL7 interfaces, enabling live time-series forecasting of patient readmission trends. Optimized large-scale clinical data processing on GCP with PySpark and BigQuery. Engineered supervised models (e.g., CVD risk) on over 250k FHIR-compliant EMR records; developed deep learning CV architectures (CNN/RNN) to analyze medical imagery for automated COPD detection. Implemented Generative AI components with Hugging Face Transformers and Med-PaLM-inspired LLMs for clinical NLP, extracting diagnostic insights from unstructured notes. Evaluated models with Scikit-learn metrics and XGBoost ensembles; applied SHAP and LIME for interpretability to reduce bias. Collaborated with clinicians to map outputs to ICD-10/SNOMED CT ontologies and ensured HIPAA/NABH compliance. Deployed inference APIs with Docker/Kubernetes on GCP and automated MLOps with Kubeflow Pipelines and Git-based CI/CD. Enf