I’m Arun Addagatla, a Founding Engineer at Lamatic.ai, focused on building robust AI platforms that move from prototype to production with speed and reliability.
- 🚀 Architecting systems across AI, backend, infrastructure, DevOps, and selected frontend workflows
- 🧠 Specialized in LLMs, Conversational AI, Agentic AI, Semantic RAG, and MLOps
- ⚙️ Passionate about high-scale inference, deployment orchestration, and enterprise integrations
- 🎓 Bachelor’s in Computer Engineering (MCT’s RGIT) with 9.6 CGPA
Founding Team Member – AI
March 2024 – Present · Miami, Florida, United States
- Built a major part of Lamatic’s core tech stack as an early employee across AI, backend, infra, and critical frontend components.
- Designed and deployed a Kubernetes ETL platform with VPC networking and multi-source ingestion (Drive, S3, SharePoint).
- Implemented OAuth systems for Google, GitHub, and Microsoft integrations.
- Engineered a serverless flow executor supporting 1M+ monthly requests.
- Built a high-throughput deployment engine handling 1K+ project deployments/minute.
- Reduced deployment time from 2 minutes to 15 seconds (87% faster).
- Implemented advanced AI infrastructure including Semantic RAG, MCP nodes, and agentic orchestration (Supervisor Node).
- Built real-time webhook systems for Slack and Microsoft Teams synchronization.
- Architected Lamatic’s VCS with native GitHub integration for automatic flow sync.
Machine Learning Engineer
October 2022 – March 2024 · Mumbai, Maharashtra, India
- Built an ML-driven system to monitor and improve user productivity.
- Engineered an AI messaging assistant for communication efficiency.
- Designed a meeting insights system using ASR-based summarization.
- Implemented customer support chatbots with OpenAI LLM models.
- Developed a semantic image search system for better retrieval quality.
Machine Learning Engineer
September 2021 – August 2022 · Bangalore Urban, Karnataka, India
- Improved text classification pipelines and optimized model performance.
- Designed CI/CD systems for ML delivery.
- Implemented drift detection and anomaly detection systems.
- Developed scalable inferencing pipelines for production workloads.
Data Science Intern
July 2020 – September 2020
- Worked on capex optimization for anomaly detection in financial transactions.
- Performed statistical analysis on 100,000+ transactions for threshold tuning.
- Collaborated across business + engineering on 80+ transaction rules.
- Built an API that accepted rule sets and generated optimized threshold outputs.
MCT’s Rajiv Gandhi Institute of Technology
Bachelor’s Degree, Computer Engineering
- Neural Network and Deep Learning
- The Data Science Course 2020: Complete Data Science Bootcamp
- IBM Data Science
- IBM Data Science – Open Source Tools
- Python Data Structures
- Building reliable GenAI + Agentic AI systems for enterprise use-cases
- Scalable deployment and inference platforms
- Robust MLOps and production AI infrastructure
- Retrieval-augmented systems and workflow automation