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I’m Anshi Rai, an AI/ML Engineer and Data Engineer based in Houston, TX, specializing in building end-to-end AI solutions across healthcare, insurance, and energy domains. I design models, engineer features, govern data, and automate ML workflows, translating business problems into production-ready systems that boost operational efficiency, compliance, and cost optimization. I enjoy collaborating with cross-functional teams and communicating insights clearly to drive tangible impact. I’m proficient in Python, SQL, Power BI, and AWS, with hands-on experience in LLMs and generative AI, including prompt engineering, fine-tuning, embeddings, and RAG pipelines for domain-specific NLP solutions. I continuously expand skills in MLOps, model governance, and retrieval-augmented generation to deliver robust, auditable AI systems.…I’m Anshi Rai, an AI/ML Engineer and Data Engineer based in Houston, TX, specializing in building end-to-end AI solutions across healthcare, insurance, and energy domains. I design models, engineer features, govern data, and automate ML workflows, translating business problems into production-ready systems that boost operational efficiency, compliance, and cost optimization. I enjoy collaborating with cross-functional teams and communicating insights clearly to drive tangible impact. I’m proficient in Python, SQL, Power BI, and AWS, with hands-on experience in LLMs and generative AI, including prompt engineering, fine-tuning, embeddings, and RAG pipelines for domain-specific NLP solutions. I continuously expand skills in MLOps, model governance, and retrieval-augmented generation to deliver robust, auditable AI systems.

Anshi Rai

Data Scientist, AI Engineer, AI Developer, +3

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I’m Anshi Rai, an AI/ML Engineer and Data Engineer based in Houston, TX, specializing in building end-to-end AI solutions across healthcare, insurance, and energy domains. I design models, engineer features, govern data, and automate ML workflows, translating business problems into production-ready systems that boost operational efficiency, compliance, and cost optimization. I enjoy collaborating with cross-functional teams and communicating insights clearly to drive tangible impact. I’m proficient in Python, SQL, Power BI, and AWS, with hands-on experience in LLMs and generative AI, including prompt engineering, fine-tuning, embeddings, and RAG pipelines for domain-specific NLP solutions. I continuously expand skills in MLOps, model governance, and retrieval-augmented generation to deliver robust, auditable AI systems.…I’m Anshi Rai, an AI/ML Engineer and Data Engineer based in Houston, TX, specializing in building end-to-end AI solutions across healthcare, insurance, and energy domains. I design models, engineer features, govern data, and automate ML workflows, translating business problems into production-ready systems that boost operational efficiency, compliance, and cost optimization. I enjoy collaborating with cross-functional teams and communicating insights clearly to drive tangible impact. I’m proficient in Python, SQL, Power BI, and AWS, with hands-on experience in LLMs and generative AI, including prompt engineering, fine-tuning, embeddings, and RAG pipelines for domain-specific NLP solutions. I continuously expand skills in MLOps, model governance, and retrieval-augmented generation to deliver robust, auditable AI systems.

Available to hire

I’m Anshi Rai, an AI/ML Engineer and Data Engineer based in Houston, TX, specializing in building end-to-end AI solutions across healthcare, insurance, and energy domains. I design models, engineer features, govern data, and automate ML workflows, translating business problems into production-ready systems that boost operational efficiency, compliance, and cost optimization. I enjoy collaborating with cross-functional teams and communicating insights clearly to drive tangible impact.

I’m proficient in Python, SQL, Power BI, and AWS, with hands-on experience in LLMs and generative AI, including prompt engineering, fine-tuning, embeddings, and RAG pipelines for domain-specific NLP solutions. I continuously expand skills in MLOps, model governance, and retrieval-augmented generation to deliver robust, auditable AI systems.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Information System Analyst / Business Analyst at Graviti Energy

November 1, 2024 - November 4, 2025

Designed and deployed data ingestion pipelines processing 5M IoT telemetry records per month using SQL and AWS S3, enabling ML readiness for downstream experiments. Built predictive maintenance models with tree-based learners and time-series data, improving failure prediction accuracy across asset types. Automated data drift and anomaly monitoring dashboards in Power BI, reducing manual oversight by 40 hours per month. Led containerized inference deployments with cross-functional teams using Docker and MLflow, increasing release frequency by 1.3x. Produced audit-ready governance documentation to ensure compliance with tax and energy data regulations.

AI/ Business Analyst at HonorHealth

May 1, 2024 - May 1, 2024

Developed an ML-based denial risk scoring prototype for over 1 million claim records, reducing manual review volume by 250,000 cases annually. Implemented NLP-based text extraction from 80,000+ clinical notes to augment structured claim features, enhancing model recall and precision. Authored functional and technical requirements for ML-enabled features and coordinated UAT cycles with clinical and QA teams. Built executive Power BI dashboards tracking claim volume, model KPIs, and adoption impact for leadership reporting.

Business Analyst / MLOps at Access Healthcare

July 1, 2022 - July 1, 2022

Built and deployed classification models on 2.1 million healthcare claims, flagging high-risk transactions and preventing revenue leakage across payer systems. Engineered ETL pipelines on AWS and SQL processing over 500,000 claim records daily, ensuring reliable model input data. Designed Tableau dashboards integrating predictive insights across 15 KPIs for operations, billing, and quality teams. Executed A/B testing across 10 regional RCM teams to measure model impact on claim cycle time and efficiency. Automated data validation and version tracking scripts using Python, reducing rework on recurring model retraining tasks. Collaborated with 5+ cross-functional teams to integrate ML outputs into decision pipelines. Mentored 2 junior analysts on ML pipeline design, feature engineering, and SHAP-based explainability. Delivered client-facing analytics reports quantifying performance uplift and cost reduction from model interventions.

AI/ Business Analyst at HonorHealth

December 1, 2023 - May 1, 2024

Developed an ML-based denial risk scoring prototype for over 1 million claim records, reducing manual reviews by 250,000 cases annually; implemented NLP-based text extraction from 80,000+ clinical notes to augment structured claim features; designed LLM-powered retrieval-augmented generation pipeline using Hugging Face, FAISS, and OpenAI to summarize clinical notes and payer documentation, cutting time-to-insight for case review by 45% and improving downstream extraction recall; authored functional and technical requirements and coordinated UAT; built executive Power BI dashboards tracking claim volume, model KPIs, and adoption impact for leadership reporting.

Business Analyst /MLOps at Access Healthcare

January 1, 2020 - July 1, 2022

Built and deployed classification models on 2.1 million healthcare claims, flagging high-risk transactions and preventing revenue leakage; engineered ETL pipelines on AWS and SQL processing over 500,000 claim records daily; designed Tableau dashboards integrating predictive insights across 15 KPIs; executed A/B testing across 10 regional RCM teams; built LLM-powered automation workflows using Hugging Face to extract and summarize payer notes and claims narratives; automated data validation and version tracking scripts to reduce rework on recurring model retraining; collaborated with cross-functional teams to integrate ML outputs and mentored junior analysts.