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Hi, I'm Manish Alluri, a data-driven AI/ML Engineer with 5+ years of experience designing, deploying, and scaling production-grade machine learning systems across healthcare, enterprise analytics, and cloud platforms. I translate complex data into executive-ready insights and lead end-to-end ML initiatives—from data engineering and model development to MLOps and cloud deployment. I thrive in fast-paced environments, take ownership, and collaborate with product managers and clinicians to align ML solutions with business KPIs. My strengths include Python, deep learning, NLP, and cloud-native architectures, and I enjoy delivering high-impact solutions that drive measurable outcomes.…Hi, I'm Manish Alluri, a data-driven AI/ML Engineer with 5+ years of experience designing, deploying, and scaling production-grade machine learning systems across healthcare, enterprise analytics, and cloud platforms. I translate complex data into executive-ready insights and lead end-to-end ML initiatives—from data engineering and model development to MLOps and cloud deployment. I thrive in fast-paced environments, take ownership, and collaborate with product managers and clinicians to align ML solutions with business KPIs. My strengths include Python, deep learning, NLP, and cloud-native architectures, and I enjoy delivering high-impact solutions that drive measurable outcomes.

Manish Alluri

Data Scientist, AI Engineer, Full Stack Developer, +4

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Hi, I'm Manish Alluri, a data-driven AI/ML Engineer with 5+ years of experience designing, deploying, and scaling production-grade machine learning systems across healthcare, enterprise analytics, and cloud platforms. I translate complex data into executive-ready insights and lead end-to-end ML initiatives—from data engineering and model development to MLOps and cloud deployment. I thrive in fast-paced environments, take ownership, and collaborate with product managers and clinicians to align ML solutions with business KPIs. My strengths include Python, deep learning, NLP, and cloud-native architectures, and I enjoy delivering high-impact solutions that drive measurable outcomes.…Hi, I'm Manish Alluri, a data-driven AI/ML Engineer with 5+ years of experience designing, deploying, and scaling production-grade machine learning systems across healthcare, enterprise analytics, and cloud platforms. I translate complex data into executive-ready insights and lead end-to-end ML initiatives—from data engineering and model development to MLOps and cloud deployment. I thrive in fast-paced environments, take ownership, and collaborate with product managers and clinicians to align ML solutions with business KPIs. My strengths include Python, deep learning, NLP, and cloud-native architectures, and I enjoy delivering high-impact solutions that drive measurable outcomes.

Available to hire

Hi, I’m Manish Alluri, a data-driven AI/ML Engineer with 5+ years of experience designing, deploying, and scaling production-grade machine learning systems across healthcare, enterprise analytics, and cloud platforms. I translate complex data into executive-ready insights and lead end-to-end ML initiatives—from data engineering and model development to MLOps and cloud deployment.

I thrive in fast-paced environments, take ownership, and collaborate with product managers and clinicians to align ML solutions with business KPIs. My strengths include Python, deep learning, NLP, and cloud-native architectures, and I enjoy delivering high-impact solutions that drive measurable outcomes.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Software Engineer (AI/ML) at Integrated BI Solutions Inc.

June 1, 2024 - Present

Led end-to-end development and production deployment of machine learning solutions, improving model delivery timelines by 15% through standardized MLOps practices. Architected and maintained MLflow-based model lifecycle management, enabling experiment tracking, versioning, and reproducibility across teams. Created robust CI/CD pipelines using GitHub Actions and Docker, cutting deployment failures by 30% and accelerating releases from days to hours. Engineered scalable SQL-driven data pipelines with validation and transformation layers, increasing data accuracy and reliability by 25%. Developed and enhanced forecasting and classification models (XGBoost, Random Forest), achieving up to 18% improvement in predictive accuracy. Delivered executive-level Power BI and Tableau dashboards, reducing reporting turnaround by 40% and supporting data-driven strategic decisions. Collaborated with product managers and stakeholders to align ML solutions with business KPIs and long-term analytics strat

Research Data Scientist at University of Florida Health

January 1, 2022 - May 1, 2024

Owned and enhanced large-scale multimodal data pipelines (fMRI, EMR, behavioral data), reducing manual preprocessing effort by 35%. Developed reproducible Python-based workflows integrating MATLAB and SPSS outputs, improving experimental consistency by 30%. Applied advanced statistical modeling and ML techniques (GLM, regression, classification) supporting multi-million-dollar NIH-funded research initiatives. Automated data ingestion from REDCap and EMR systems via Python APIs, decreasing data latency by 25%. Performed deep EDA on high-dimensional neuroimaging datasets, identifying anomalies and improving preprocessing robustness by 20%. Partnered with clinicians and researchers to translate analytical findings into actionable clinical and research insights.

Machine Learning Engineer at Nexova

January 1, 2020 - July 1, 2021

Planned and deployed production-grade ML applications using TensorFlow, FastAPI, and Flask, increasing deployment success rates by 35%. Built and streamlined NLP pipelines with Hugging Face and NLTK, driving 15% gains in model performance across text analytics use cases. Developed an ML-based fraud detection platform using LSTM and Variational Autoencoders (VAE), improving anomaly detection accuracy by 30%. Tuned and improved PostgreSQL and MySQL databases for ML workloads, reducing query latency by 40%. Deployed scalable ML solutions on AWS and GCP, achieving 25% infrastructure cost savings through resource optimization. Implemented automated CI/CD pipelines with Jenkins and GitHub Actions, shrinking deployment cycles from weeks to hours.