Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

Hi, I’m Raghuram Reddy Gayam, an AI/ML Engineer with around five years of experience building and deploying machine learning and NLP systems across fintech and healthcare domains. I enjoy turning data into practical business outcomes through production-grade ML pipelines, real-time inference APIs, and scalable deployment architectures using Python, TensorFlow, AWS, FastAPI, and Hugging Face. I specialize in deep learning, time-series forecasting, NLP, MLOps, data quality, and AI agents; I also fine-tune LLMs and build generative AI apps and AI agents for automation.…Hi, I’m Raghuram Reddy Gayam, an AI/ML Engineer with around five years of experience building and deploying machine learning and NLP systems across fintech and healthcare domains. I enjoy turning data into practical business outcomes through production-grade ML pipelines, real-time inference APIs, and scalable deployment architectures using Python, TensorFlow, AWS, FastAPI, and Hugging Face. I specialize in deep learning, time-series forecasting, NLP, MLOps, data quality, and AI agents; I also fine-tune LLMs and build generative AI apps and AI agents for automation.

Raghuram Reddy Gayam

AI Engineer, AI Developer, Data Scientist, +2





Hi, I’m Raghuram Reddy Gayam, an AI/ML Engineer with around five years of experience building and deploying machine learning and NLP systems across fintech and healthcare domains. I enjoy turning data into practical business outcomes through production-grade ML pipelines, real-time inference APIs, and scalable deployment architectures using Python, TensorFlow, AWS, FastAPI, and Hugging Face. I specialize in deep learning, time-series forecasting, NLP, MLOps, data quality, and AI agents; I also fine-tune LLMs and build generative AI apps and AI agents for automation.…Hi, I’m Raghuram Reddy Gayam, an AI/ML Engineer with around five years of experience building and deploying machine learning and NLP systems across fintech and healthcare domains. I enjoy turning data into practical business outcomes through production-grade ML pipelines, real-time inference APIs, and scalable deployment architectures using Python, TensorFlow, AWS, FastAPI, and Hugging Face. I specialize in deep learning, time-series forecasting, NLP, MLOps, data quality, and AI agents; I also fine-tune LLMs and build generative AI apps and AI agents for automation.

Available to hire

Hi, I’m Raghuram Reddy Gayam, an AI/ML Engineer with around five years of experience building and deploying machine learning and NLP systems across fintech and healthcare domains.

I enjoy turning data into practical business outcomes through production-grade ML pipelines, real-time inference APIs, and scalable deployment architectures using Python, TensorFlow, AWS, FastAPI, and Hugging Face. I specialize in deep learning, time-series forecasting, NLP, MLOps, data quality, and AI agents; I also fine-tune LLMs and build generative AI apps and AI agents for automation.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Work Experience

AI/ML Engineer at UnitedHealth Group, USA

December 1, 2023 - Present

Developed ML models for healthcare claims classification, improving triage accuracy by 26%. Built real-time inference APIs using FastAPI deployed on AWS Lambda, reducing latency by 35%. Created NLP pipelines for medical entity recognition using spaCy and Transformers. Integrated Large Language Models (OpenAI GPT-4, LLaMA) with LangChain and LlamaIndex for automated patient query resolution and document summarization. Automated data validation and model retraining workflows with Airflow and MLflow. Deployed LLM-powered RAG pipelines for healthcare FAQs using FAISS-based vector search and OpenAI embeddings. Built dashboards for monitoring drift and accuracy using Seaborn and Power BI. Ensured compliance with HIPAA and data privacy regulations during model deployment.

Machine Learning Engineer at FIS, India

June 30, 2023 - July 24, 2025

Designed fraud detection models using Logistic Regression and XGBoost, reducing false positives by 30%. Developed NLP solutions for transaction classification using spaCy and NLTK. Explored early adoption of AI agents and prompt-based automation for intelligent report generation using OpenAI GPT-3.5. Created and orchestrated ML pipelines using Pandas, Airflow, and SQLAlchemy. Built scalable APIs for model inference using Flask and FastAPI, deployed on AWS Lambda. Implemented version control for models using MLflow, improving reproducibility and traceability. Reduced feature processing time by 40% through optimized data pipelines. Led internal code reviews and automated test workflows using PyTest and GitHub Actions. Contributed to proof-of-concept projects using LangChain for conversational AI prototypes and internal knowledge base agents.

AI/ML Engineer at UnitedHealth Group

December 1, 2023 - Present

Engineered ML models for claims classification and fraud detection, improving triage accuracy by 26% and reducing financial leakage. Automated claims adjudication with FastAPI on AWS Lambda, cutting latency by 35% and accelerating reimbursements. Built NLP pipelines for ICD/CPT mapping and EOB parsing to reduce denials. Deployed LLM-powered RAG queries for claims support, lowering costs and improving SLA. Fine-tuned GPT/LLaMA models on healthcare data for better reimbursement summaries. Developed provider/patient chat/voice bots via Azure AI, and implemented responsible AI practices with bias checks and dashboards. Mentored juniors and explored LangChain-based AI agents to automate support.

Machine Learning Engineer at FIS

June 1, 2023 - October 7, 2025

Designed fraud detection models (Logistic Regression, XGBoost) reducing false positives by 30%. Built NLP solutions for transaction classification with spaCy/NLTK. Created ML pipelines with Pandas, Airflow, and SQLAlchemy; deployed APIs with Flask/FastAPI on AWS Lambda. Used MLflow for model versioning and GitHub Actions for CI. Enhanced transaction classification with Hugging Face Transformers; implemented a RAG pipeline using FAISS and GPT-3.5 for real-time analysis. Integrated LangChain agents for automatic case triaging, cutting manual reviews. Mentored teammates and contributed to internal AI knowledge-sharing.