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I'm Jisvitha Athaluri, an AI/ML Engineer with 5 years of experience building and deploying production machine learning systems across insurance, mobility, and industrial domains. I design scalable real-time and batch inference pipelines using Python, PyTorch, SQL, and Spark, and I excel at end-to-end ML lifecycle workflows. I have hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, model monitoring, and governance, including Bayesian hyperparameter optimization, drift detection, and A/B testing. I thrive collaborating with claims, compliance, and research teams to ship reliable, low-latency ML applications in production.…I'm Jisvitha Athaluri, an AI/ML Engineer with 5 years of experience building and deploying production machine learning systems across insurance, mobility, and industrial domains. I design scalable real-time and batch inference pipelines using Python, PyTorch, SQL, and Spark, and I excel at end-to-end ML lifecycle workflows. I have hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, model monitoring, and governance, including Bayesian hyperparameter optimization, drift detection, and A/B testing. I thrive collaborating with claims, compliance, and research teams to ship reliable, low-latency ML applications in production.

Jisvitha Athaluri

AI Engineer, Data Scientist, Data Analyst, +2

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I'm Jisvitha Athaluri, an AI/ML Engineer with 5 years of experience building and deploying production machine learning systems across insurance, mobility, and industrial domains. I design scalable real-time and batch inference pipelines using Python, PyTorch, SQL, and Spark, and I excel at end-to-end ML lifecycle workflows. I have hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, model monitoring, and governance, including Bayesian hyperparameter optimization, drift detection, and A/B testing. I thrive collaborating with claims, compliance, and research teams to ship reliable, low-latency ML applications in production.…I'm Jisvitha Athaluri, an AI/ML Engineer with 5 years of experience building and deploying production machine learning systems across insurance, mobility, and industrial domains. I design scalable real-time and batch inference pipelines using Python, PyTorch, SQL, and Spark, and I excel at end-to-end ML lifecycle workflows. I have hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, model monitoring, and governance, including Bayesian hyperparameter optimization, drift detection, and A/B testing. I thrive collaborating with claims, compliance, and research teams to ship reliable, low-latency ML applications in production.

Available to hire

I’m Jisvitha Athaluri, an AI/ML Engineer with 5 years of experience building and deploying production machine learning systems across insurance, mobility, and industrial domains. I design scalable real-time and batch inference pipelines using Python, PyTorch, SQL, and Spark, and I excel at end-to-end ML lifecycle workflows.

I have hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, model monitoring, and governance, including Bayesian hyperparameter optimization, drift detection, and A/B testing. I thrive collaborating with claims, compliance, and research teams to ship reliable, low-latency ML applications in production.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

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, 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.