Generative AI Engineer with 6+ Years Experience

Sai Keerthi

Generative AI / Machine Learning Engineer

Cumming, GA **********************@*****.*** 404-***-**** LinkedIn Professional Summary

Generative AI and Machine Learning Engineer with 6+ years of experience designing, deploying, and scaling production- grade AI systems across healthcare, education, and enterprise domains. Specialized in Generative AI, LLMOps, RAG systems, and MLOps, with strong hands-on expertise in LangChain, OpenAI, Databricks, MLflow, Azure ML, and AWS SageMaker. Proven track record of delivering secure, scalable, and cost-optimized GenAI solutions that drive measurable business impact.

Core Skills

Generative AI & LLM Systems: RAG Architectures, LangChain, LangGraph, Prompt Engineering, Fine-Tuning, Embeddings, Vector Databases (FAISS, Chroma, Pinecone), OpenAI / Azure OpenAI, Tool Calling, Agents, Hallucination Mitigation, LLM Evaluation (RAGAS, BLEU, ROUGE)

Machine Learning & Deep Learning: Python, PyTorch, TensorFlow, Scikit-learn, XGBoost, NLP, Transformers, Model Evaluation

MLOps & LLMOps: MLflow, Databricks, Kubeflow, Airflow, CI/CD Pipelines, Model Versioning, Monitoring & Drift Detection, Feature Engineering

Cloud & Deployment: AWS (SageMaker), Azure ML, GCP, Docker, Kubernetes, FastAPI, Flask, REST APIs Data Engineering & Analytics: SQL, PySpark, ETL Pipelines, Data Preprocessing, Power BI, Tableau Observability & Monitoring: Prometheus, Grafana, Logging & Alerting Professional Experience

Machine Learning Engineer -Generative AI

Nirvana Health, RxAdvance Corporation, MA Feb 2023 – Present

End-to-end development of a HIPAA-compliant, production-grade RAG-based GenAI assistant using LangChain, OpenAI GPT models, and vector databases, supporting 1,000+ internal users and reducing document retrieval time by 60%.

Deployed scalable ML pipelines using Databricks and MLflow, reducing training and experimentation time by 35%.

Designed semantic search and retrieval pipelines using embeddings and vector indexing to improve answer relevance and reduce hallucinations.

Developed deep learning and predictive ML models for patient analytics, improving diagnostic accuracy by 18%.

Implemented LLMOps workflows including prompt versioning, evaluation, monitoring, and cost optimization for enterprise GenAI systems.

Optimized LLM inference latency and API costs through caching, batching, and token usage analysis.

Containerized and deployed ML and GenAI services using Docker and Kubernetes via Azure ML endpoints.

Automated large-scale data ingestion and preprocessing pipelines using Airflow and PySpark across multi- terabyte datasets.

Built RESTful APIs using FastAPI to integrate GenAI capabilities into internal platforms.

Implemented monitoring, logging, and alerting for models and GenAI services using Prometheus and Grafana.

Collaborated cross-functionally with DevOps, security, and product teams to deliver compliant, scalable GenAI solutions.

Volunteering Work/Graduate Researcher

Kennesaw State University, GA Aug 2021 – Dec 2022

Built and evaluated machine learning and NLP models as part of graduate research and advanced coursework.

Designed end-to-end ML workflows including data preprocessing, feature engineering, training, and deployment.

Gained hands-on experience with cloud-based ML platforms, MLflow tracking, and model deployment best practices.

Worked on applied projects involving deep learning, transformers, and scalable data processing. Machine Learning Engineer Jun 2019 – Nov 2021

Kendriya Vidyalaya, India

Designed and deployed ML models to analyze student performance and predict attendance trends, improving intervention accuracy by 25%.

Developed classification and regression models to identify at-risk students and forecast academic outcomes.

Built data preprocessing, feature extraction, and validation pipelines using Python, Pandas, and NumPy.

Performed exploratory data analysis and delivered insights through Power BI and Matplotlib dashboards for academic leadership.

Automated SQL-based reporting workflows, reducing manual data processing time by 30%.

Implemented feature selection and model tuning to improve prediction stability across academic terms.

Designed modular ML pipelines to support periodic retraining as new data became available.

Collaborated with IT teams to migrate data pipelines to Azure Cloud, enabling scalable and real-time data access.

Documented ML workflows and collaborated with stakeholders to translate predictions into actionable interventions.

Key Projects

HIPAA-Compliant Enterprise GenAI Assistant (RAG + LLMOps)

Developed a secure, enterprise-grade GenAI chatbot integrating OpenAI GPT models with internal policy and

medical documentation.

Implemented retrieval-augmented generation, prompt optimization, and evaluation pipelines.

Reduced average response latency by 60% while ensuring compliance and auditability. Real-Time Healthcare Claims Risk Prediction System

Built an XGBoost-based ML system to predict claims denial and fraud probability using historical and streaming data.

Deployed models using AWS SageMaker and integrated predictions into internal dashboards, improving operational efficiency by 35%.

Academic Performance Prediction Platform

Designed ML algorithms to forecast student grades and attendance patterns, improving early intervention accuracy by 20%.

Education:

MS in Information Technology, Kennesaw State University 2022

Bachelors of Technology in Electrical, Electronics and Communications Engineering, Gandhi Institute of Technology and Management (GITAM) 2019

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