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I’m an AI Infrastructure Engineer with 3+ years of experience building cloud-native observability and reliability frameworks, integrating LLMs into production pipelines, and deploying managed platform solutions for enterprise and media environments. I’ve worked across Oracle, Accenture, and IBM to design scalable APIs, CI/CD systems, and monitoring stacks that keep AI services performant, secure, and easy to operate. I enjoy taking complex AI and data workflows from design to production—instrumenting them with Prometheus/Grafana and CloudWatch, building event-driven pipelines, and delivering fast, measurable outcomes like improved latency, uptime, and incident response times. I’m especially passionate about enabling reliable, efficient AI solutions for broadcast-technology and media software environments, combining strong engineering fundamentals with clear documentation and mentorship.…I’m an AI Infrastructure Engineer with 3+ years of experience building cloud-native observability and reliability frameworks, integrating LLMs into production pipelines, and deploying managed platform solutions for enterprise and media environments. I’ve worked across Oracle, Accenture, and IBM to design scalable APIs, CI/CD systems, and monitoring stacks that keep AI services performant, secure, and easy to operate. I enjoy taking complex AI and data workflows from design to production—instrumenting them with Prometheus/Grafana and CloudWatch, building event-driven pipelines, and delivering fast, measurable outcomes like improved latency, uptime, and incident response times. I’m especially passionate about enabling reliable, efficient AI solutions for broadcast-technology and media software environments, combining strong engineering fundamentals with clear documentation and mentorship.

Harshith Kumar Audipudi

AI Engineer, Software Engineer, Data Engineer, +2

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I’m an AI Infrastructure Engineer with 3+ years of experience building cloud-native observability and reliability frameworks, integrating LLMs into production pipelines, and deploying managed platform solutions for enterprise and media environments. I’ve worked across Oracle, Accenture, and IBM to design scalable APIs, CI/CD systems, and monitoring stacks that keep AI services performant, secure, and easy to operate. I enjoy taking complex AI and data workflows from design to production—instrumenting them with Prometheus/Grafana and CloudWatch, building event-driven pipelines, and delivering fast, measurable outcomes like improved latency, uptime, and incident response times. I’m especially passionate about enabling reliable, efficient AI solutions for broadcast-technology and media software environments, combining strong engineering fundamentals with clear documentation and mentorship.…I’m an AI Infrastructure Engineer with 3+ years of experience building cloud-native observability and reliability frameworks, integrating LLMs into production pipelines, and deploying managed platform solutions for enterprise and media environments. I’ve worked across Oracle, Accenture, and IBM to design scalable APIs, CI/CD systems, and monitoring stacks that keep AI services performant, secure, and easy to operate. I enjoy taking complex AI and data workflows from design to production—instrumenting them with Prometheus/Grafana and CloudWatch, building event-driven pipelines, and delivering fast, measurable outcomes like improved latency, uptime, and incident response times. I’m especially passionate about enabling reliable, efficient AI solutions for broadcast-technology and media software environments, combining strong engineering fundamentals with clear documentation and mentorship.

Available to hire

I’m an AI Infrastructure Engineer with 3+ years of experience building cloud-native observability and reliability frameworks, integrating LLMs into production pipelines, and deploying managed platform solutions for enterprise and media environments. I’ve worked across Oracle, Accenture, and IBM to design scalable APIs, CI/CD systems, and monitoring stacks that keep AI services performant, secure, and easy to operate.

I enjoy taking complex AI and data workflows from design to production—instrumenting them with Prometheus/Grafana and CloudWatch, building event-driven pipelines, and delivering fast, measurable outcomes like improved latency, uptime, and incident response times. I’m especially passionate about enabling reliable, efficient AI solutions for broadcast-technology and media software environments, combining strong engineering fundamentals with clear documentation and mentorship.

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Work Experience

AI Infrastructure Engineer – Cloud Native Systems at Oracle

May 1, 2025 - Present

Architected and deployed a cloud-native healthcare data platform using Java and Spring Boot for real-time EHR interoperability, reducing data latency by 35%. Built observability frameworks with Prometheus, Grafana, and AWS CloudWatch to monitor AI model performance and reduce MTTD from 15 minutes to under 2 minutes. Integrated LLMs via Python and FastAPI to automate clinical documentation, cutting manual effort by 40%. Developed managed cloud platform (MCP) solutions on AWS using Docker and Kubernetes with self-healing deployments and autoscaling, achieving 99.95% uptime for AI workloads. Implemented REST APIs with Spring Boot and Helidon to handle 50K+ transactions per second with sub-100ms latency, and set up CI/CD using GitHub Actions and ArgoCD for canary deployments and rollback strategies with zero-downtime releases. Mentored junior engineers to apply AI infrastructure best practices, improving their ramp-up time by 40%.

Software Engineer – AI & Data Infrastructure at Accenture

December 1, 2022 - July 31, 2024

Owned 24/7 production support for Presto Metrolinx fare payment ML pipelines, monitoring distributed data workflows and maintaining 99.9% SLA compliance for transit-critical operations. Diagnosed API timeouts and pipeline bottlenecks in microservices architectures using Prometheus, Grafana, and AWS CloudWatch, completing root cause analysis within 30 minutes. Managed end-to-end model deployments via Bamboo and Bitbucket CI/CD, containerizing inference workloads with Docker and Kubernetes. Optimized real-time ETL pipelines on Apache Spark and Kafka to process 10M+ transit events per day with sub-second latency for fare processing. Implemented distributed logging and tracing using the ELK stack to improve incident response time by 50% through centralized log correlation. Enforced RBAC and data governance across AWS and Dataiku environments to support HIPAA-like compliance for transit data.

Data Engineer – Data Pipelines at IBM

January 1, 2022 - December 31, 2022

Built scalable data integration pipelines using Informatica and Ab Initio to move large datasets into cloud warehouses with automated quality checks for enterprise BI reporting. Developed and tuned ETL/ELT workflows, optimizing SQL queries and Python scripts to reduce processing time by 30% while ensuring data integrity. Containerized legacy ETL jobs into Docker and orchestrated them on Kubernetes to enable elastic scaling and reduce execution failures by 40%. Designed RESTful APIs using Flask to expose curated data products for downstream microservices with low latency and high availability. Implemented monitoring dashboards with Grafana to track pipeline health and data drift, alerting teams to anomalies within minutes. Contributed to Agile sprint cycles by documenting pipeline architecture and data flow diagrams in Confluence and mentoring interns in cloud-native data engineering practices.