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AI/ML & Generative AI Engineer with 4+ years of experience designing, building, and deploying scalable machine learning, deep learning, and LLM-powered solutions across healthcare and enterprise domains. Experienced in end-to-end ML pipelines, computer vision systems, NLP applications, and cloud-based AI platforms. Proven track record of delivering production-ready AI solutions, automating complex workflows, improving operational efficiency, and deploying scalable MLOps pipelines in fast-paced Agile environments.…AI/ML & Generative AI Engineer with 4+ years of experience designing, building, and deploying scalable machine learning, deep learning, and LLM-powered solutions across healthcare and enterprise domains. Experienced in end-to-end ML pipelines, computer vision systems, NLP applications, and cloud-based AI platforms. Proven track record of delivering production-ready AI solutions, automating complex workflows, improving operational efficiency, and deploying scalable MLOps pipelines in fast-paced Agile environments.

Hanuman Muchanapally

AI Engineer, Data Scientist, Data Analyst, +2

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AI/ML & Generative AI Engineer with 4+ years of experience designing, building, and deploying scalable machine learning, deep learning, and LLM-powered solutions across healthcare and enterprise domains. Experienced in end-to-end ML pipelines, computer vision systems, NLP applications, and cloud-based AI platforms. Proven track record of delivering production-ready AI solutions, automating complex workflows, improving operational efficiency, and deploying scalable MLOps pipelines in fast-paced Agile environments.…AI/ML & Generative AI Engineer with 4+ years of experience designing, building, and deploying scalable machine learning, deep learning, and LLM-powered solutions across healthcare and enterprise domains. Experienced in end-to-end ML pipelines, computer vision systems, NLP applications, and cloud-based AI platforms. Proven track record of delivering production-ready AI solutions, automating complex workflows, improving operational efficiency, and deploying scalable MLOps pipelines in fast-paced Agile environments.

Available to hire

AI/ML & Generative AI Engineer with 4+ years of experience designing, building, and deploying scalable machine learning, deep learning, and LLM-powered solutions across healthcare and enterprise domains. Experienced in end-to-end ML pipelines, computer vision systems, NLP applications, and cloud-based AI platforms.

Proven track record of delivering production-ready AI solutions, automating complex workflows, improving operational efficiency, and deploying scalable MLOps pipelines in fast-paced Agile environments.

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Intermediate

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Work Experience

AI/ML Engineer at Tempus

October 1, 2024 - Present

Engineered TensorFlow CNN models for medical imaging using PostgreSQL-managed metadata and built clinical risk prediction models that improved triage throughput by ~1,800 studies per month and generated ~$450K annual operational efficiency gains. Implemented GPT-4/Claude NLP pipelines with RAG to extract structured clinical insights, reducing manual review by ~15 hours per week and improving report consistency by 22%. Optimized YOLOv8 and Detectron2 computer vision pipelines integrated with Kafka streaming to reduce review latency by ~10 minutes per study and improve prioritization across ~1,500 urgent clinical workflows monthly. Applied SHAP and LIME for interpretability, tracked experiments via MLflow/Model Registry, and automated retraining using Apache Airflow for reliable production performance across 45,000+ imaging records per month. Deployed GAN-based augmentation and LSTM models with transfer learning on AWS SageMaker, using Apache Spark to preprocess data and automate 1,100+

AI/ML Engineer at Deloitte

January 1, 2022 - December 1, 2023

Architected XGBoost and CatBoost regression models for KPI forecasting, improving prediction accuracy by 22% and reducing forecast variance by 18%. Automated document classification using NER with Azure Databricks and MySQL, reducing misclassification errors by 28% and accelerating compliance workflows for ~1.5M pages per month. Implemented reinforcement learning algorithms in simulated decision environments to evaluate trade-offs and optimize sequential decision-making, reducing forecasting error by 14% across 1,000+ planning scenarios. Built CI/CD pipelines using GitHub Actions, Docker, and Kubernetes within Azure ML, reducing deployment failures by 12%, shortening release cycles by 35%, and saving 6,000+ manual deployment hours annually. Monitored ML pipeline health using Datadog, Azure Monitor, and anomaly detection, maintaining 99.5% uptime and resolving 3,000+ alerts annually. Delivered Power BI dashboards improving leadership visibility and reducing decision latency by 37% while

Associate Machine Learning Engineer at Deloitte

September 1, 2020 - December 1, 2021

Built data preprocessing workflows with Pandas, NumPy, and MongoDB to reduce feature extraction errors by 18% and process 400k+ operational records daily in real-time ML pipelines. Developed recommendation models using PyTorch and neural networks to improve personalization of enterprise reporting dashboards by 24%. Applied BERT-based text classification and summarization, improving classification accuracy by 21% for compliance and client reporting tasks. Created evaluation dashboards in Jupyter to analyze hyperparameter impacts, improving model performance by 27% and reducing underperformance in low-volume segments by 15%. Supported containerized ML inference services using Docker, maintained code with Git, and monitored systems using Prometheus and Grafana, assisting live A/B testing for 110k daily inference requests.