Hi, I'm Sarath LingareddyBuilding Production-Ready
AI Systems.
About Me
I am an AI Engineer focused on building production-grade AI systems that solve real-world problems at scale. My work spans the full lifecycle of machine learning, from research and prototyping to deployment and monitoring.
I specialize in LLM orchestration, RAG pipelines, and scalable ML deployment on AWS. I bring an architecture-first mindset to every project, ensuring systems are not just functional but resilient, observable, and ready for production traffic.
Holding multiple AWS certifications including the Machine Learning Specialty, I combine deep cloud infrastructure expertise with hands-on ML engineering to deliver end-to-end AI solutions.
role: AI Engineer
focus:
- LLM Systems
- RAG Pipelines
- Production ML
cloud: AWS (Certified)
approach: Architecture-first
status: Building the futureLLM Orchestration
Designing and deploying large language model systems with RAG pipelines for production environments.
Production ML
Building end-to-end ML pipelines that scale, with focus on reliability, monitoring, and continuous improvement.
Cloud-Native
AWS-certified engineer building cloud-native AI architectures with infrastructure as code and CI/CD.
Systems Thinking
Architecting distributed systems that handle real-world complexity with low latency and high availability.
Experience
AI Engineer Intern
Agile PathLed the development of an intelligent document retrieval system leveraging multimodal RAG architecture. Designed and implemented a two-stage retrieval pipeline that combined embedding-based search with re-ranking for maximum relevance across diverse document formats.
Key Achievements
Built two-stage multimodal RAG system using Muvera + ColPali architecture
Achieved 90% retrieval relevance across 2,000+ enterprise documents
90%Reduced manual document lookup time by 50% through intelligent automation
50%Maintained 99%+ service availability across all deployed systems
99%+System Stack
Featured Projects
Vigil3D
Video Violence Detection Platform
The Problem
Security monitoring systems lack real-time automated violence detection, requiring constant human surveillance which is costly and error-prone.
Approach
Developed a 3D CNN model leveraging spatiotemporal features for accurate video classification. Built a full-stack platform with a React + TypeScript frontend and FastAPI backend for real-time inference.
Reducing Hallucinations in QA Chatbots
Fine-tuned LLM with DPO
The Problem
LLM-based QA chatbots frequently generate plausible but incorrect answers (hallucinations), undermining user trust and reliability in production systems.
Approach
Fine-tuned TinyLLaMA using Direct Preference Optimization (DPO) to align model outputs with factual responses. Conducted comparative analysis between SFT and DPO training strategies.
Skills & Technologies
AI / ML
LLM Systems
Production ML
Cloud & Infra
Programming
Certifications
AWS Machine Learning Specialty
Amazon Web Services
Validated expertise in building, training, tuning, and deploying ML models on AWS.
AWS Developer Associate
Amazon Web Services
Proficiency in developing, deploying, and debugging cloud-based applications using AWS services.
Terraform Associate
HashiCorp
Infrastructure as Code expertise for provisioning and managing cloud infrastructure at scale.
Let's Build Something Together
I'm currently open to new opportunities in AI engineering. Whether you have a project in mind or just want to connect, I'd love to hear from you.
Say Hello