AI Engineer - Distributed Systems & LLM Pipelines

Abhinay Dodda

AI Engineer ************@*****.*** 314-***-****

SUMMARY

4+ years of experience in software engineering focused on building scalable distributed systems and cloud-native applications. Proficient in developing high- performance solutions using Python, C++, and AWS, optimizing data processing and analysis pipelines. Experienced in architecting monitoring and introspection tools to enhance system performance and reliability, enabling engineers to quickly analyze and troubleshoot systems. Strong knowledge of Kubernetes and microservices architecture ensures efficient deployment and resource management. Ability to discover performance bottlenecks, continuously improving system efficiency and scalability. Enthusiastically engaged in collaborative engineering, embodying best practices in API development and technical problem-solving to drive project success.

SKILLS

• Cloud Platforms: AWS, Google Cloud Platform, Azure, EC2, S3, Lambda

• Additional Skills: WeRide One, Linux, Test Automation

• Programming Languages: C++, Go, Java, Python, Scala, SQL

• Containers & Orchestration: Docker, Helm, Kubernetes, LXC, kubectl, Amazon EKS, CI/CD EXPERIENCE

Scale AI, United States Nov 2024 - Present

AI Engineer

Architected and deployed LLM pipelines with Python and AWS to enhance model serving capabilities. This architecture facilitated efficient processing of large- scale data while improving overall system responsiveness and throughput. Developed modules for RAG architectures using vector search and prompt engineering technologies to optimize inference. This contribution enabled real-time access to AI models and ultimately improved user engagement and satisfaction. Implemented advanced monitoring and profiling tools, giving engineers insights into system performance issues and allowing quick introspection of operations. This initiative helped identify and resolve bottlenecks, leading to enhanced efficiency across multiple AI-driven projects. Designed and constructed scalable data ingestion frameworks to support high-volume workloads for analytics processing. These frameworks effectively handled millions of data points daily, ensuring robust analytics capabilities for diverse product teams. Optimized cloud infrastructure for AI system deployments through effective use of distributed deep learning techniques. This optimization decreased latency significantly, allowing for faster and more efficient model training and inference. Zoho, Chennai, India Jul 2022 - Dec 2023

Software Engineer

Developed and enhanced APIs within distributed services using Java and Python to improve communication across microservices. This work streamlined data flow and enabled more efficient service interactions for various application components. Collaborated in designing scalable cloud-native applications on AWS, focusing on performance optimization and reliability. This contributed to a significant increase in system resilience, handling high loads efficiently during peak usage times. Conducted code reviews and implemented best practices for testing and debugging to improve overall code quality. By fostering a collaborative engineering environment, the team reduced defects and enhanced durability in production systems. Built comprehensive monitoring tools using Python and Grafana to provide real-time insights into application performance. This facilitated quicker troubleshooting and performance enhancements, helping maintain elevated user satisfaction levels. Participated in optimizing deployment processes through CI/CD pipelines, improving release cycles with automated testing. This streamlined approach allowed for monthly releases, increasing feature delivery speed by over 30%. Freshworks, Chennai, India Jan 2021 - Jun 2022

Software Engineer

Developed core functionalities for customer engagement software with a strong focus on API development using Node.js. This contributed to a robust platform that could support thousands of concurrent users effectively. Designed and implemented backend services on cloud platforms, improving scalability and efficiency across applications. This work significantly impacted operational efficiency, managing over 100,000 daily transactions seamlessly. Implemented performance monitoring and troubleshooting solutions in existing applications to identify and resolve inefficiencies. This initiative allowed for increased responsiveness and faster processing times in user interactions. Led collaborative engineering sessions to address complex technical challenges regarding system performance and reliability. This resulted in innovative solutions that were integrated into the main product, enhancing overall user experience. Worked on continuous integration solutions to enhance deployment strategies, ensuring that software updates were timely and effective. This approach led to zero downtime during critical deployment phases.

EDUCATION

Master of Science in Computer Science (GPA: 3.84) Jan 2024 - Dec 2025 Saint Louis University, Saint Louis, Missouri

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