📄 Resume

Sai Kiran Gandluri

SUMMARY

AI/ML Engineer (SME) with 5+ years of experience designing and deploying enterprise-scale machine learning and Generative AI solutions across healthcare, insurance, and IT consulting domains, with strong expertise in clinical workflows, claims analytics, and patient engagement systems.
Proven expertise in cloud-native AI on AWS, including SageMaker, Bedrock, LangChain, Redshift, and MLOps pipelines using Docker, Kubernetes, and CI/CD, delivering scalable, secure, and compliant solutions aligned with HIPAA, CMS, and ERM standards.
Strong research and engineering background with EMNLP 2025 publication, hands-on experience in NLP, LLMs, fraud detection, risk modeling, and analytics-driven decision-making, combined with Agile leadership and cross-functional collaboration to drive measurable business impact.

EDUCATION

University at Massachusetts Lowell

Master of Science in Computer Software

USA

Dec 2025

Achievements: Published "Chatbot To Help Patients Understand Their Health" paper, recently accepted to EMNLP 2025 Findings.
Coursework: Algorithms, Data Mining and Data Science, Program Structures, Advance Big Data Indexing, Design Patterns

Geethanjali College of Engineering and Technology

Bachelor of Technology in Computer Science

India

Jun 2020

TECHNICAL SKILLS

Methodology: SDLC, Agile, Waterfall
Languages: Python, C, C++, Java, R, SQL, MATLAB, HTML, CSS, JavaScript, TypeScript
Frameworks: TensorFlow, PyTorch, Keras, NumPy, Pandas, Scikit-Learn, Matplotlib
IDEs: Visual Studio Code, PyCharm, Jupyter Notebook
Machine Learning: Linear-logistic regression, Clustering, SVM, PCA, Random Forest, Boosting, Lasso, Ridge, CNN, RNN
Deep Learning: TensorFlow, Keras
AWS Services: Amazon SageMaker, Bedrock (retrieveAndGenerate, retrieve), AWS Comprehend, Amazon Polly, AWS Transcribe, AWS Rekognition, S3, Glue, Redshift, RDS, EC2, Lambda, AWS IAM, AWS Shield, AWS CloudFormation, Terraform, AWS CloudWatch, AWS X-Ray
Data Management: S3, Glue, Amazon Redshift, RDS
Compute Services: Amazon EC2, AWS Lambda
General Cloud and DevOps Skills: Cloud Architecture, CI/CD with CodePipeline, CodeBuild, CodeDeploy, Monitoring with CloudWatch, AWS X-Ray
Version Control Tools: Git, GitHub, GitLab
Operating Systems: Windows, Linux, Mac
Soft Skills: Problem-Solving, Communication, Collaboration

WORK EXPERIENCE

S&P Global

Software Dev Engineer, AI

Jul 2025 – Present

CA, USA

Designed and implemented scalable ETL and data integration pipelines using Java 8, Python, supporting downstream ML analytics, anomaly detection, processed 1M+ monthly records, large-scale datasets (100B+ transactions) using Hadoop, Spark, and Hive.
Engineered and deployed real-time microservices using Spring Boot, REST APIs, PostgreSQL, Redis, and API Gateway, achieving sub-200ms response times and enabling secure merchant/global payment integrations with Spring Security and OAuth2.
Built and productionized machine learning–driven anomaly detection systems and model inference services, integrating data pipelines with Snowflake warehousing, governance controls, and optimized SQL performance improvements of 40%.
Developed enterprise-grade integrations using Boomi (SFTP, JDBC, SOAP) and automated API synchronization across ERP and business systems, implemented Swagger/OpenAPI documentation for standardized endpoint governance.
Contributed to AI-enabled applications by translating reference architectures, proof-of-concepts, and sample workflows into functional software components, built customer-facing demos showcasing data analytics and ML capabilities.
Collaborated in Agile/Scrum environments, performing unit, integration testing, debugging production issues, supporting UAT/Preprod transitions, automating alerting workflows, mentoring peers on API development, Python optimization, data engineering best practices.

University of Massachusetts

Research Assistant

Aug 2024 - Jun 2025

Lowell, MA

Applied machine learning techniques to engineer a cutting-edge patient education platform combining Flask and PostgreSQL, underpinned by literature-driven methodologies, including multi-agent large language model (LLM) frameworks and reinforcement learning alignment, reducing annotation cycle time by 30%.
Synthesized state-of-the-art research (NoteAid-Chatbot, LLaMA, PaniniQA) into an operationalized LLM dialogue system for domain-personalized, multi-turn medical conversations across diverse demographics, designed and conducted experiments on advanced prompting and role-play strategies (instruction-based, summarization, anti-redundancy), deploying and evaluating approaches that boosted patient clarity and knowledge retention via human/model-aligned testing.
Authored comprehensive QA, experiment documentation, and technical reports, translated ambiguous research findings into actionable analyses and influenced stakeholders through storytelling with data, collaborated across global teams to align technical execution with the latest trends in conversational healthcare AI, contributing to a publication accepted at EMNLP 2025.
Improved programming skills for 88 students, with 86% demonstrating better project development skills through personalized instruction on data structures and object-oriented programming during tutoring sessions.
Conducted interactive online workshops and designed engaging assignments grounded in experiment design, improving student participation by 20% while enhancing technical skills, project creativity, and comprehension through practical coding challenges.

Cognizant

Software Engineer

Jul 2021 – Nov 2023

Hyderabad, India

Developed wireless network management applications using React.js and Node.js, creating intuitive dashboards for monitoring Wi-Fi infrastructure across airports and public venues, improving network reliability by 35%.
Implemented real-time network monitoring solutions using WebSocket connections and Chart.js, providing live analytics on network performance, user traffic, and system health to operations teams.
Built containerized backend services using Docker and AWS ECS, enabling high-availability APIs with automated service scaling.
Designed and implemented internal monitoring dashboards using React.js and D3.js, visualizing key performance indicators, system logs, and error rates in real time for engineering stakeholders.
Developed and maintained AWS Lambda functions integrated with CloudWatch to automate backup processes, alert generation, and task scheduling, improving observability and reducing downtime incidents.
Authored robust Bash scripts and cron jobs for log rotation, system cleanup, and database snapshot management in Linux-based infrastructure, improving operational stability and disaster recovery compliance.

PROJECTS

Bakery Website (React + Node.js + MySQL)

May 2025 - Jun 2025

Designed and deployed a responsive, full-stack web application with dynamic product pages and order management.
Built from scratch within 3 days to help a local street vendor expand his bakery's digital presence.
Improved load times by 40% across devices and directly contributed to a 15% increase in the vendor's sales by reaching new customers online.

Real-time detection and Analysis of Fake News on Social-Media

Apr 2024 - May 2025

Developed an NLP system using BERT + SBERT embeddings to detect misinformation on social media with 91.1% accuracy, 0.89 precision, 0.85 recall, and 0.87 F1 score, validated through ROC curve and confusion matrix analysis.
Built an interactive real-time dashboard (Streamlit + Hugging Face) enabling users to classify news articles into multiple truth categories ("true," "false,” "pants-fire," etc.), empowering fact-checkers to combat misinformation instantly.

Deepfake Face Detection

Jul 2024 - Aug 2024

Developed a deepfake detection model using CNN-LSTM, achieving 85% accuracy. Applied image processing techniques to detect inconsistencies in facial features, leveraging computer vision methodologies to enhance security applications.
Implemented frame-level analysis to uncover subtle temporal irregularities, focusing on eye blinking patterns and facial artifacts, resulting in a 30% improvement in detection precision.

Cloud-Based 3-Tier Architecture

Engineered and deployed a scalable 3-tier system on AWS (React frontend, Node.js/Express middleware, MySQL backend), cutting latency by 30%, boosting query performance by 40%.
Automated CI/CD pipelines and containerized deployments with Docker and AWS Code Pipeline, reducing release cycles from days to minutes and lowering operational costs by 25% through right-sizing and auto-scaling.

PUBLICATIONS

Chatbot To Help Patients Understand Their Health. EMNLP 2025 Findings (Author) - Developed scalable AI role-play systems improving patient comprehension and reducing annotation time by 30%.