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Hi, I’m JISVITHA ATHALURI, an AI/ML Engineer with 5 years of experience delivering production-grade ML systems across insurance, mobility, and industrial domains. I design and deploy scalable real-time and batch inference pipelines using Python, PyTorch, SQL, and Spark to support high-volume enterprise workloads. I enjoy collaborating with cross-functional teams to turn data into reliable, measurable business impact. In my work, I have built and operationalized RAG pipelines, implemented ML lifecycle governance, and delivered low-latency model serving at scale. I am passionate about translating complex problems into robust ML solutions, reducing latency and costs, and ensuring reproducibility and SLA compliance in production environments.…Hi, I’m JISVITHA ATHALURI, an AI/ML Engineer with 5 years of experience delivering production-grade ML systems across insurance, mobility, and industrial domains. I design and deploy scalable real-time and batch inference pipelines using Python, PyTorch, SQL, and Spark to support high-volume enterprise workloads. I enjoy collaborating with cross-functional teams to turn data into reliable, measurable business impact. In my work, I have built and operationalized RAG pipelines, implemented ML lifecycle governance, and delivered low-latency model serving at scale. I am passionate about translating complex problems into robust ML solutions, reducing latency and costs, and ensuring reproducibility and SLA compliance in production environments.

Jisvitha Athaluri

AI Engineer, Data Scientist, Data Analyst, +4

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Hi, I’m JISVITHA ATHALURI, an AI/ML Engineer with 5 years of experience delivering production-grade ML systems across insurance, mobility, and industrial domains. I design and deploy scalable real-time and batch inference pipelines using Python, PyTorch, SQL, and Spark to support high-volume enterprise workloads. I enjoy collaborating with cross-functional teams to turn data into reliable, measurable business impact. In my work, I have built and operationalized RAG pipelines, implemented ML lifecycle governance, and delivered low-latency model serving at scale. I am passionate about translating complex problems into robust ML solutions, reducing latency and costs, and ensuring reproducibility and SLA compliance in production environments.…Hi, I’m JISVITHA ATHALURI, an AI/ML Engineer with 5 years of experience delivering production-grade ML systems across insurance, mobility, and industrial domains. I design and deploy scalable real-time and batch inference pipelines using Python, PyTorch, SQL, and Spark to support high-volume enterprise workloads. I enjoy collaborating with cross-functional teams to turn data into reliable, measurable business impact. In my work, I have built and operationalized RAG pipelines, implemented ML lifecycle governance, and delivered low-latency model serving at scale. I am passionate about translating complex problems into robust ML solutions, reducing latency and costs, and ensuring reproducibility and SLA compliance in production environments.

Available to hire

Hi, I’m JISVITHA ATHALURI, an AI/ML Engineer with 5 years of experience delivering production-grade ML systems across insurance, mobility, and industrial domains. I design and deploy scalable real-time and batch inference pipelines using Python, PyTorch, SQL, and Spark to support high-volume enterprise workloads. I enjoy collaborating with cross-functional teams to turn data into reliable, measurable business impact.
In my work, I have built and operationalized RAG pipelines, implemented ML lifecycle governance, and delivered low-latency model serving at scale. I am passionate about translating complex problems into robust ML solutions, reducing latency and costs, and ensuring reproducibility and SLA compliance in production environments.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI/ML Engineer at Globe Life

October 1, 2025 - Present

Implemented a Retrieval-Augmented Generation (RAG) pipeline using LangChain, Pinecone, and LLaMA models. Reduced average policy query resolution time by 38% in collaboration with claims and compliance teams. Engineered LoRA-based fine-tuning workflows in PyTorch for insurance-specific QA datasets, achieving 94% domain accuracy and reducing inference cost via quantization and vLLM optimization. Deployed model serving infrastructure with FastAPI, Docker, Kubernetes, and AWS SageMaker endpoints, supporting 500+ concurrent requests with sub-2-second latency. Established LLM evaluation frameworks to measure hallucinations and factual consistency, lowering hallucinations from 12% to 3%. Optimized hybrid retrieval (dense + sparse) to improve document precision by 27% across 500K+ indexed records. Automated ML CI/CD using MLflow and Git workflows, enabling 40% faster releases. Implemented streaming document ingestion via Spark pipelines for real-time embedding refresh and retrieval consistency

Machine Learning Engineer at Texas A&M University

October 1, 2024 - September 1, 2025

Developed a semantic search platform using PyTorch embeddings and ChromaDB; indexed 50K+ papers and cut faculty literature review time by 60%. Fine-tuned BERT and RoBERTa for Named Entity Recognition achieving 93% F1. Built RESTful inferences with FastAPI and Docker for TensorFlow-based T5 summarization models, enabling production-ready NLP services across departments. Designed cross-validation and evaluation workflows to improve reproducibility; optimized Bayesian hyperparameter tuning to increase classification accuracy by 8% and stabilize performance across folds. Containerized ML workloads and conducted A/B testing to enhance retrieval relevance by 22%.

Machine Learning Engineer at Texas A&M University, Corpus Christi

October 1, 2024 - September 30, 2025

Developed a semantic search platform using PyTorch embedding models and ChromaDB, indexing 50K+ academic papers to reduce faculty literature review time by 60%. Fine-tuned BERT and RoBERTa for Named Entity Recognition, achieving 93% F1 and streamlining metadata tagging. Built RESTful inference APIs with FastAPI and Docker for TensorFlow-based T5 summarization models, enabling cross-department ML services. Created cross-validation and evaluation workflows to improve reproducibility; enhanced Bayesian optimization to boost classification accuracy by 8% and stabilize performance. Containerized workloads with Docker and standardized deployments across compute environments. Conducted prompt engineering AB tests, achieving higher zero-shot relevance. Improved embedding strategies and retrieval metrics, boosting semantic search relevance by 22%.

Machine Learning Engineer at Uber

June 1, 2021 - January 31, 2024

Developed ML lifecycle automation within Michelangelo, supporting thousands of production fraud detection and ETA models at scale. Implemented Bayesian hyperparameter tuning pipelines for scheduled retraining, achieving 11% performance gains while reducing manual optimization. Integrated drift detection for real-time inference, reducing degraded model exposure time by 25%. Standardized feature engineering within a shared feature store, improving consistency and reducing training-serving skew by 22%. Established MLflow-based experiment tracking with automated retraining, cutting rollback incidents by 20%. Optimized batch inference with PySpark for retraining workflows and standardized SQL-driven performance validation. Built production monitoring dashboards tracking latency, drift, and data freshness to prevent SLA breaches.

Data Scientist at DXC Technology

May 1, 2020 - May 31, 2021

Developed transformer-based QA system over 500K+ technical documents with PyTorch and Elasticsearch. Fine-tuned GPT-2 on equipment maintenance manuals, reducing documentation drafting time by 40%. Built NER pipelines for equipment data and high-precision entity extraction. Created GAN-based synthetic data generation for rare failure scenarios, improving anomaly detection recall by 15%. Engineered Spark-based sensor data pipelines for 1200+ industrial assets, enabling scalable predictive maintenance modeling. Automated daily ETL with Airflow, improving pipeline reliability. Coordinated validation with reliability engineers to ensure operational readiness before deployment.