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I'm an AI/ML Engineer with nearly 3 years of experience designing, deploying, and optimizing large-scale machine learning systems across healthcare, insurance, and enterprise domains. I specialize in end-to-end ML pipelines, real-time data engineering workflows, and deploying production-grade models using Docker, Kubernetes, and MLOps practices. I work with Spark, Kafka, TensorFlow, PyTorch, and SQL, with a strong focus on explainability, scalability, and measurable business impact. I enjoy collaborating with cross-functional teams to translate complex problems into scalable, auditable AI solutions.…I'm an AI/ML Engineer with nearly 3 years of experience designing, deploying, and optimizing large-scale machine learning systems across healthcare, insurance, and enterprise domains. I specialize in end-to-end ML pipelines, real-time data engineering workflows, and deploying production-grade models using Docker, Kubernetes, and MLOps practices. I work with Spark, Kafka, TensorFlow, PyTorch, and SQL, with a strong focus on explainability, scalability, and measurable business impact. I enjoy collaborating with cross-functional teams to translate complex problems into scalable, auditable AI solutions.

Raghava Kalyan Inguva

Data Scientist, AI Engineer, AI Developer, +2

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I'm an AI/ML Engineer with nearly 3 years of experience designing, deploying, and optimizing large-scale machine learning systems across healthcare, insurance, and enterprise domains. I specialize in end-to-end ML pipelines, real-time data engineering workflows, and deploying production-grade models using Docker, Kubernetes, and MLOps practices. I work with Spark, Kafka, TensorFlow, PyTorch, and SQL, with a strong focus on explainability, scalability, and measurable business impact. I enjoy collaborating with cross-functional teams to translate complex problems into scalable, auditable AI solutions.…I'm an AI/ML Engineer with nearly 3 years of experience designing, deploying, and optimizing large-scale machine learning systems across healthcare, insurance, and enterprise domains. I specialize in end-to-end ML pipelines, real-time data engineering workflows, and deploying production-grade models using Docker, Kubernetes, and MLOps practices. I work with Spark, Kafka, TensorFlow, PyTorch, and SQL, with a strong focus on explainability, scalability, and measurable business impact. I enjoy collaborating with cross-functional teams to translate complex problems into scalable, auditable AI solutions.

Available to hire

I’m an AI/ML Engineer with nearly 3 years of experience designing, deploying, and optimizing large-scale machine learning systems across healthcare, insurance, and enterprise domains.

I specialize in end-to-end ML pipelines, real-time data engineering workflows, and deploying production-grade models using Docker, Kubernetes, and MLOps practices. I work with Spark, Kafka, TensorFlow, PyTorch, and SQL, with a strong focus on explainability, scalability, and measurable business impact. I enjoy collaborating with cross-functional teams to translate complex problems into scalable, auditable AI solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at UnitedHealth Group

January 1, 2025 - Present

Architected scalable ML pipelines on AWS/GCP, improving fraud detection accuracy by 12%, reducing false positives by 18%, and accelerating retraining by 25%, delivering measurable efficiency and enterprise-wide cost savings. Built scalable feature pipelines using Spark, Kafka, and SQL across 10M+ records for ETL and normalization, improving throughput by 40% and data quality by 25%, while aligning cross-functional KPIs including precision, recall, latency, and AUC-ROC. Developed and fine-tuned NLP models using Transformers (BERT, ClinicalBERT) on unstructured EHR/claims data, improving entity extraction by 20% and enabling classification/summarization pipelines that reduced manual document review time by 35% while sustaining F1-score above 0.90. Optimized ML models through hyperparameter tuning, pruning, and quantization, reducing inference latency by 35% and memory footprint by 40%, with predictive accuracy above 90% across diverse datasets. Deployed production-grade ML microservices

AI/ML Engineer at Aspire Technologies

May 1, 2021 - June 1, 2023

Designed, developed, and deployed ML solutions including classification, anomaly detection, and recommendation systems, improving predictive accuracy by 15% and automating workflows by 30%. Built scalable ML pipelines for ingestion, pre-processing, and deployment with Spark and SQL, improving data reliability by 25% and reducing delivery time by 20%. Developed NLP pipelines using Transformers (BERT, RoBERTa) for text classification and entity recognition, boosting document processing accuracy by 18% and reducing manual review time by 25% across compliance-driven workflows. Engineered custom summarization and semantic search leveraging Hugging Face and spaCy, improving query response accuracy by 22% and enabling scalable knowledge retrieval across regulated enterprise data environments. Optimized models via prototyping, hyperparameter tuning, and cross-validation, reducing overfitting by 30% and maintaining predictive accuracy above 90%. Deployed production-grade ML microservices with D