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AI/ML Engineer with 4+ years of experience designing, developing, and deploying scalable machine learning models and intelligent automation solutions across healthcare and enterprise technology domains. Proficient in data preprocessing, feature engineering, model development, and hyperparameter tuning, with experience building robust ML pipelines using Python, TensorFlow, and PyTorch. Skilled in leveraging cloud platforms (AWS, Azure) and applying MLOps practices to deploy production-ready models. Experienced in applying machine learning to clinical/patient datasets for predictive analytics and delivering actionable insights through data visualization and cross-functional collaboration in Agile environments.…AI/ML Engineer with 4+ years of experience designing, developing, and deploying scalable machine learning models and intelligent automation solutions across healthcare and enterprise technology domains. Proficient in data preprocessing, feature engineering, model development, and hyperparameter tuning, with experience building robust ML pipelines using Python, TensorFlow, and PyTorch. Skilled in leveraging cloud platforms (AWS, Azure) and applying MLOps practices to deploy production-ready models. Experienced in applying machine learning to clinical/patient datasets for predictive analytics and delivering actionable insights through data visualization and cross-functional collaboration in Agile environments.

Venkat Edara

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

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AI/ML Engineer with 4+ years of experience designing, developing, and deploying scalable machine learning models and intelligent automation solutions across healthcare and enterprise technology domains. Proficient in data preprocessing, feature engineering, model development, and hyperparameter tuning, with experience building robust ML pipelines using Python, TensorFlow, and PyTorch. Skilled in leveraging cloud platforms (AWS, Azure) and applying MLOps practices to deploy production-ready models. Experienced in applying machine learning to clinical/patient datasets for predictive analytics and delivering actionable insights through data visualization and cross-functional collaboration in Agile environments.…AI/ML Engineer with 4+ years of experience designing, developing, and deploying scalable machine learning models and intelligent automation solutions across healthcare and enterprise technology domains. Proficient in data preprocessing, feature engineering, model development, and hyperparameter tuning, with experience building robust ML pipelines using Python, TensorFlow, and PyTorch. Skilled in leveraging cloud platforms (AWS, Azure) and applying MLOps practices to deploy production-ready models. Experienced in applying machine learning to clinical/patient datasets for predictive analytics and delivering actionable insights through data visualization and cross-functional collaboration in Agile environments.

Available to hire

AI/ML Engineer with 4+ years of experience designing, developing, and deploying scalable machine learning models and intelligent automation solutions across healthcare and enterprise technology domains. Proficient in data preprocessing, feature engineering, model development, and hyperparameter tuning, with experience building robust ML pipelines using Python, TensorFlow, and PyTorch.

Skilled in leveraging cloud platforms (AWS, Azure) and applying MLOps practices to deploy production-ready models. Experienced in applying machine learning to clinical/patient datasets for predictive analytics and delivering actionable insights through data visualization and cross-functional collaboration in Agile environments.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at Scale AI

September 1, 2024 - Present

Designed, developed, and deployed scalable AI/ML systems using Azure ML and Azure Databricks, increasing data labeling throughput by 22% and saving over 1,500 manual hours annually across multiple projects. Built transformer-based NLP models with Hugging Face and PyTorch, boosting annotation accuracy by 14% and reducing approximately 2,000 labeling errors per quarter. Automated end-to-end ML workflows with Apache Airflow and GitHub Actions, enabling 26 releases and cutting retraining/deployment time by 40%. Implemented parameter-efficient LLM fine-tuning with LoRA and containerized inference services with Docker and Kubernetes, improving uptime to 99.7% and reducing downtime by 150 hours. Integrated SHAP and LIME explainability into Power BI dashboards to surface model behavior for data-driven decisions. Developed CV pipelines with YOLOv8/Detectron2, improving object detection by 18%. Benchmarked LLMs (GPT-4, Claude, Mistral) with Cursor AI and LangChain, reducing inference costs by 20

AI Engineer at Tempus

March 1, 2023 - August 1, 2024

Built and deployed ML solutions using AWS SageMaker and Apache Airflow for predictive analytics and time series forecasting on patient datasets, reducing manual processing by 31% and saving 3,000+ operational hours. Developed transformer-based document summarization workflows for medical reports using BERT, FAISS, and Pinecone, automating 85% of oncology documentation reviews. Designed automated retraining pipelines with MLflow and GitHub Actions to track model drift and versioning for diagnostic models, improving prediction stability by 35%, reducing SLA breaches by 25%, and saving approximately $95K annually. Improved end-to-end ML pipeline performance by refining feature engineering and optimizing inference, reducing latency by 40% for ~75,000 records/day. Applied SHAP explainability to complex diagnostic models and visualized results in Power BI dashboards with HIPAA-compliant handling. Deployed and monitored models using Kubeflow and Seldon for scalable serving and continuous perf

Associate Machine Learning Engineer at HCLTech

April 1, 2020 - August 1, 2021

Developed and fine-tuned predictive maintenance models using scikit-learn and XGBoost, reducing downtime by 12% and saving approximately $200K annually. Built BERT-based NLP pipelines for automated invoice processing, boosting entity extraction accuracy to 85% and reducing manual review by 37%. Automated ETL workflows using AWS Lambda and AWS Glue, improving data freshness by 30% and reliably processing around 200K records daily. Maintained high code quality with pytest and Git, reducing bugs by 25% and preventing 120+ critical incidents, improving release stability within Agile teams. Engineered speech recognition pipelines and developed Power BI dashboards to monitor KPIs, improving processing accuracy by 15%.