# Sai Vikas Reddy Yeddulamala

> AI/ML Engineer | GenAI, RAG, LLMs | Forecasting, NLP, Anomaly Detection | MLOps on AWS, Azure, Databricks | DP-100 Certified

Location: Raleigh, North Carolina, United States
Profile: https://flows.cv/saivikasreddyyeddulamala

I’m an AI/ML Engineer with 3+ years of experience building and deploying production-grade machine learning and large language model (LLM) systems across healthcare and financial services. I specialize in designing end-to-end pipelines—from data ingestion and feature engineering to scalable deployment and monitoring—using cloud-native and distributed architectures.

In my current role, I focus on solving high-impact business problems such as demand forecasting, compliance automation, and real-time decision systems. I’ve built transformer-based forecasting models, production-ready retrieval systems using hybrid search, and high-throughput inference platforms optimized with Kubernetes and GPU acceleration. My work has led to measurable improvements in model accuracy, latency, and operational efficiency at scale.

I have hands-on expertise in LLM systems, including RAG architectures, vector search, and fine-tuning techniques like LoRA/QLoRA. I enjoy working at the intersection of machine learning, systems engineering, and product development—turning complex data into actionable intelligence.

Previously, I developed fraud detection systems using graph-based modeling, built LLM-powered customer support assistants, and designed experimentation frameworks to ensure reliable model deployment in production environments.

💡 Key areas of interest:
• Applied AI & LLM systems
• Scalable MLOps & distributed data pipelines
• Real-time ML systems & inference optimization
• NLP, retrieval systems, and intelligent automation

I’m always open to collaborating on innovative AI projects and exploring opportunities where machine learning can drive meaningful impact.

Let’s connect!

## Work Experience
### ML Engineer @ Cardinal Health
Jan 2024 – Present | United States
• Designed and deployed a transformer-based demand forecasting system on AWS, integrating historical sales, seasonality signals, and external variables; improved forecast accuracy by 15% and reduced stockout incidents by 20% across supply chain operations
• Built a production-grade retrieval system for regulatory and compliance documents using hybrid search (dense + lexical), reducing document lookup time by 40% and significantly improving decision turnaround for compliance teams
• Engineered high-throughput model serving infrastructure using Kubernetes (EKS) and optimized inference via batching and GPU acceleration, increasing real-time inference throughput by 10x+ under peak loads
• Developed end-to-end MLOps pipelines with automated training, validation, and deployment using MLflow and CI/CD workflows, reducing model release cycles from weeks to days and improving deployment reliability
• Implemented distributed data pipelines using Spark and Kafka to process ~8M daily records, enabling near real-time feature generation and reducing data latency by 50%s

### Data Scientist @ Tata Consultancy Services
Jan 2022 – Jan 2023 | Hyderabad
• Built and deployed an LLM-powered customer support assistant using retrieval-augmented generation, improving query resolution accuracy and increasing customer satisfaction (NPS) by 12%
• Developed a fraud detection system combining graph-based embeddings and gradient boosting models, improving precision at high recall thresholds and reducing false positives in real-time scoring pipelines
• Designed online experimentation frameworks, including shadow deployments, canary releases, and threshold calibration to control false positive rates below 1%, reducing production risk and rollback frequency
• Standardized feature engineering pipelines and introduced reusable feature storage on distributed systems, reducing model retraining cycles and improving consistency across fraud and risk models

### Junior Data Scientist @ Tata Consultancy Services
Jan 2021 – Jan 2022 | Hyderabad
• Developed churn and credit risk models using gradient boosting techniques with engineered temporal and behavioral features, contributing to measurable reductions in churn and high-risk approvals
• Built recommendation models for next-best-offer systems using candidate generation and ranking approaches, improving conversion rates in A/B testing experiments
• Optimized ETL pipelines with incremental processing and validation checks, reducing runtime and improving reliability of downstream analytics systems


## Education
### Master's degree in Computer Science
North Carolina State University

### Bachelor of Technology in Electrical, Electronics and Communications Engineering
GITAM Deemed University


## Contact & Social
- LinkedIn: https://linkedin.com/in/saivikasy

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Source: https://flows.cv/saivikasreddyyeddulamala
JSON Resume: https://flows.cv/saivikasreddyyeddulamala/resume.json
Last updated: 2026-04-17