Ruthvik Varma Gadiraju

About Ruthvik Varma Gadiraju

AI/ML & Generative AI Engineer with 11+ years of experience delivering end-to-end AI, machine learning, and data science solutions across financial services, healthcare, and enterprise automation domains. Strong background in designing, developing, and deploying scalable AI/ML systems, with deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AIOps, and intelligent automation.Experienced in building production-grade ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, monitoring, and drift detection using AWS SageMaker, Azure ML, MLflow, and cloud-native CI/CD pipelines. Hands-on with Generative AI use cases including GPT-powered applications, RAG-based search systems, prompt engineering, fine-tuning techniques, and API-driven integrations for enterprise workflows. Strong understanding of stateless vs stateful LLM architectures, embedding strategies, vector databases, and guardrails for production LLM systems.Proficient in NLP, OCR, and Computer Vision, applying transformer-based models, CNNs, RNNs, and deep learning techniques for document intelligence, entity extraction, classification, and image-based analytics. Designed and implemented OCR + NLP pipelines to extract structured insights from unstructured documents, improving automation accuracy and decision-making. Experienced in predictive analytics, anomaly detection, and risk modeling, supporting fraud detection, operational monitoring, and performance optimization initiatives.Strong experience in AIOps and intelligent process automation, building event-driven architectures that integrate cloud services, observability tools, and ServiceNow to automate incident management, CMDB updates, and IT operations workflows. Proven ability to reduce downtime, improve MTTR, and enhance system reliability through AI-driven monitoring and automation.Technically strong in Python, SQL, and machine learning frameworks including TensorFlow, PyTorch, and Scikit-learn, with hands-on experience in big data technologies such as Spark and PySpark. Extensive cloud experience across AWS, Azure, and GCP, leveraging services for scalable AI deployment, real-time analytics, and enterprise integrations. Comfortable working in Agile environments, collaborating with cross-functional teams including cloud, security, operations, and business stakeholders.Actively open to new opportunities where I can apply my expertise in AI/ML, Generative AI, and enterprise-scale systems to build impactful, scalable, and production-ready solutions.

Experience

1. Ai Ml Engineer

   Morgan Stanley

   Jan 2024 — Present · NY, US

   Working as an AI/ML Engineer on enterprise-scale AI, Generative AI, and AIOps platforms supporting high-volume financial and trading systems. Designing and deploying machine learning models on AWS SageMaker to predict anomalies, performance degradation, and potential system failures across distributed cloud and on-prem environments. Building AIOps pipelines that correlate logs, metrics, and events using CloudWatch, Dynatrace, and Quantum Metrics to enable proactive monitoring and reduce mean-time-to-resolution. Developing event-driven architectures using AWS Lambda, EventBridge, and Kinesis to automate incident creation, escalation, and ServiceNow CMDB updates. Creating REST and GraphQL APIs to integrate operational data across enterprise applications, monitoring tools, and cloud services. Implementing OCR and NLP pipelines using Tesseract, PaddleOCR, transformers, and OpenCV to extract structured information from unstructured documents and improve automation accuracy. Integrating AI-driven RPA workflows to automate ticket triage, validation, and remediation, significantly reducing manual intervention. Building real-time operational dashboards using Amazon QuickSight and Power BI to visualize system health, AI-driven insights, and performance trends. Establishing CI/CD pipelines for ML model training, validation, and deployment using AWS CodePipeline, CodeBuild, and CodeDeploy. Monitoring model performance, latency, drift, and system health using CloudWatch, X-Ray, and SNS alerts. Leveraging transfer learning techniques on GPU-enabled EC2 instances to accelerate model development. Supporting hybrid architectures connecting on-prem infrastructure with AWS cloud services for unified operational visibility. Collaborating closely with cloud, security, and operations teams to deliver scalable, secure, and production-ready AI solutions that improve reliability, automation, and operational efficiency across enterprise financial platforms.

Education

• Rowan University

  Master's Degree, Data science

• KL University

  Bachelor's Degree, Electrical, Electronics and Communications Engineering