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I am an AI/ML engineer with over 4 years of experience designing, building, and deploying AI solutions across healthcare, life sciences, and enterprise domains. I specialize in deep learning, NLP, computer vision, predictive modeling, and generative AI, and I routinely architect end-to-end data pipelines, MLOps workflows, and cloud-based deployments. I thrive on translating complex business and clinical requirements into reliable, high-impact AI/ML solutions. I excel at feature engineering, model optimization, interpretability, and performance monitoring, and I collaborate closely with cross-functional teams to deliver measurable outcomes. I am proficient in deploying scalable inference services, maintaining reproducible experimentation, and driving continuous improvement through robust monitoring and retraining pipelines.…I am an AI/ML engineer with over 4 years of experience designing, building, and deploying AI solutions across healthcare, life sciences, and enterprise domains. I specialize in deep learning, NLP, computer vision, predictive modeling, and generative AI, and I routinely architect end-to-end data pipelines, MLOps workflows, and cloud-based deployments. I thrive on translating complex business and clinical requirements into reliable, high-impact AI/ML solutions. I excel at feature engineering, model optimization, interpretability, and performance monitoring, and I collaborate closely with cross-functional teams to deliver measurable outcomes. I am proficient in deploying scalable inference services, maintaining reproducible experimentation, and driving continuous improvement through robust monitoring and retraining pipelines.

Avinash Nagabelly

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

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I am an AI/ML engineer with over 4 years of experience designing, building, and deploying AI solutions across healthcare, life sciences, and enterprise domains. I specialize in deep learning, NLP, computer vision, predictive modeling, and generative AI, and I routinely architect end-to-end data pipelines, MLOps workflows, and cloud-based deployments. I thrive on translating complex business and clinical requirements into reliable, high-impact AI/ML solutions. I excel at feature engineering, model optimization, interpretability, and performance monitoring, and I collaborate closely with cross-functional teams to deliver measurable outcomes. I am proficient in deploying scalable inference services, maintaining reproducible experimentation, and driving continuous improvement through robust monitoring and retraining pipelines.…I am an AI/ML engineer with over 4 years of experience designing, building, and deploying AI solutions across healthcare, life sciences, and enterprise domains. I specialize in deep learning, NLP, computer vision, predictive modeling, and generative AI, and I routinely architect end-to-end data pipelines, MLOps workflows, and cloud-based deployments. I thrive on translating complex business and clinical requirements into reliable, high-impact AI/ML solutions. I excel at feature engineering, model optimization, interpretability, and performance monitoring, and I collaborate closely with cross-functional teams to deliver measurable outcomes. I am proficient in deploying scalable inference services, maintaining reproducible experimentation, and driving continuous improvement through robust monitoring and retraining pipelines.

Available to hire

I am an AI/ML engineer with over 4 years of experience designing, building, and deploying AI solutions across healthcare, life sciences, and enterprise domains. I specialize in deep learning, NLP, computer vision, predictive modeling, and generative AI, and I routinely architect end-to-end data pipelines, MLOps workflows, and cloud-based deployments. I thrive on translating complex business and clinical requirements into reliable, high-impact AI/ML solutions.

I excel at feature engineering, model optimization, interpretability, and performance monitoring, and I collaborate closely with cross-functional teams to deliver measurable outcomes. I am proficient in deploying scalable inference services, maintaining reproducible experimentation, and driving continuous improvement through robust monitoring and retraining pipelines.

Skills

Experience Level

Expert

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Intermediate

Work Experience

AI/ML Engineer at Flatiron Health

September 1, 2024 - Present

Developed oncology-focused survival prediction models using XGBoost and regression on structured EHR data; implemented GPT-4–driven clinical NLP pipelines in a LangChain-based RAG setup to extract structured oncology variables from unstructured notes; evaluated BERT-based oncology entity extraction and benchmarked summarization against Claude; applied SHAP/LIME for interpretability; managed experiments with MLflow and Model Registry; automated retraining with Apache Airflow; deployed Docker-based inference services on AWS SageMaker for batch scoring of oncology datasets (~20k records/month).

Machine Learning Intern at IQVIA Holdings Inc.

May 1, 2024 - August 1, 2024

Developed Time Series Forecasting models using scikit-learn to analyze clinical trial enrollment variability; performed distributed feature engineering on high-volume life sciences datasets with Apache Spark on Azure Databricks; built compliance-focused Text Classification models using logistic regression on MySQL datasets (~120,000 records).

AI/ML Engineer at Deloitte

March 1, 2021 - June 30, 2022

Architected XGBoost and CatBoost regression and clustering models for KPI forecasting, improving prediction accuracy by 22% and reducing forecast variance by 18% across portfolio projections. Automated document classification pipelines using NER deployed on Azure Databricks, with outputs stored in MySQL, reducing misclassification errors by 28% in regulatory workflows. Integrated CI/CD with GitHub Actions, Docker, and Kubernetes on Azure ML, reducing failures by 12%, shortening release cycles by 35%, and saving 6,000+ manual deployment hours annually. Monitored ML pipelines on a shared MLOps platform with Datadog and Azure Monitor, sustaining 99.5% uptime and triaging 3,000+ alerts annually. Implemented Reinforcement Learning models in simulated decision environments to evaluate policy trade-offs, reducing forecasting error by 14% across 1,000+ scenarios. Delivered Power BI dashboards for leadership visibility, reducing decision latency.

Associate Machine Learning Engineer at Deloitte

December 1, 2019 - February 28, 2021

Developed recommendation models using PyTorch and neural networks to improve personalization of enterprise reporting dashboards by 24%. Built data preprocessing workflows with Pandas, NumPy, and MongoDB, reducing feature extraction errors by 18% while handling over 400k operational records daily. Applied Text Classification and Summarization using BERT, improving classification accuracy by 21% in compliance workflows. Developed model evaluation dashboards to analyze hyperparameter sensitivity and retraining performance, improving stability across low-volume client segments by 15%. Supported containerized ML inference services with Docker and Git, monitored system health using Prometheus and Grafana, and assisted in live A/B testing workflows handling approximately 110k daily inference requests.