Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I am an AI/ML Engineer with 4 years of experience designing and deploying scalable AI and ML solutions for enterprise environments. I specialize in predictive modeling, deep learning, graph neural networks, anomaly detection, and real-time data processing. I have built LLM-based systems, retrieval-augmented generation pipelines, and agentic AI workflows, and I bring a strong background in cloud ML Ops, model deployment, and production-grade AI systems using Python and SQL to manage end-to-end lifecycle from data ingestion to production. In my work, I partner with cross-functional teams to translate complex sensor data and logs into robust AI-enabled diagnostics, predictive maintenance, and intelligent decision-support tools. I thrive in fast-paced environments and enjoy applying innovative ML techniques to real-world automotive, manufacturing, and connected-vehicle use cases.…I am an AI/ML Engineer with 4 years of experience designing and deploying scalable AI and ML solutions for enterprise environments. I specialize in predictive modeling, deep learning, graph neural networks, anomaly detection, and real-time data processing. I have built LLM-based systems, retrieval-augmented generation pipelines, and agentic AI workflows, and I bring a strong background in cloud ML Ops, model deployment, and production-grade AI systems using Python and SQL to manage end-to-end lifecycle from data ingestion to production. In my work, I partner with cross-functional teams to translate complex sensor data and logs into robust AI-enabled diagnostics, predictive maintenance, and intelligent decision-support tools. I thrive in fast-paced environments and enjoy applying innovative ML techniques to real-world automotive, manufacturing, and connected-vehicle use cases.

Vamshi Krishna Konyala

AI Engineer, Data Scientist, AI Developer, +2





I am an AI/ML Engineer with 4 years of experience designing and deploying scalable AI and ML solutions for enterprise environments. I specialize in predictive modeling, deep learning, graph neural networks, anomaly detection, and real-time data processing. I have built LLM-based systems, retrieval-augmented generation pipelines, and agentic AI workflows, and I bring a strong background in cloud ML Ops, model deployment, and production-grade AI systems using Python and SQL to manage end-to-end lifecycle from data ingestion to production. In my work, I partner with cross-functional teams to translate complex sensor data and logs into robust AI-enabled diagnostics, predictive maintenance, and intelligent decision-support tools. I thrive in fast-paced environments and enjoy applying innovative ML techniques to real-world automotive, manufacturing, and connected-vehicle use cases.…I am an AI/ML Engineer with 4 years of experience designing and deploying scalable AI and ML solutions for enterprise environments. I specialize in predictive modeling, deep learning, graph neural networks, anomaly detection, and real-time data processing. I have built LLM-based systems, retrieval-augmented generation pipelines, and agentic AI workflows, and I bring a strong background in cloud ML Ops, model deployment, and production-grade AI systems using Python and SQL to manage end-to-end lifecycle from data ingestion to production. In my work, I partner with cross-functional teams to translate complex sensor data and logs into robust AI-enabled diagnostics, predictive maintenance, and intelligent decision-support tools. I thrive in fast-paced environments and enjoy applying innovative ML techniques to real-world automotive, manufacturing, and connected-vehicle use cases.

Available to hire

I am an AI/ML Engineer with 4 years of experience designing and deploying scalable AI and ML solutions for enterprise environments. I specialize in predictive modeling, deep learning, graph neural networks, anomaly detection, and real-time data processing. I have built LLM-based systems, retrieval-augmented generation pipelines, and agentic AI workflows, and I bring a strong background in cloud ML Ops, model deployment, and production-grade AI systems using Python and SQL to manage end-to-end lifecycle from data ingestion to production.

In my work, I partner with cross-functional teams to translate complex sensor data and logs into robust AI-enabled diagnostics, predictive maintenance, and intelligent decision-support tools. I thrive in fast-paced environments and enjoy applying innovative ML techniques to real-world automotive, manufacturing, and connected-vehicle use cases.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/ML Engineer at Bosch North America

July 1, 2024 - Present

Led development of an AI-driven Predictive Maintenance and Fault Detection Platform for automotive and industrial systems using Graph Neural Networks, time-series modeling, and multimodal sensor fusion to detect early-stage equipment failures across connected vehicle and smart manufacturing environments. Improved anomaly detection and failure prediction accuracy by 32% using Graph Intelligence Systems (GraphSAGE, GAT, node embeddings). Built real-time AI data pipelines on AWS and edge systems (Kinesis, IoT Core, Lambda, S3, SageMaker) for telemetry ingestion and streaming analytics. Implemented AI-powered diagnostic assistants using LangChain & vector databases enabling natural language queries over industrial knowledge graphs, reducing troubleshooting time by 27%. Optimized edge AI models for embedded ECUs with transformer-based and lightweight CNN architectures for real-time defect detection under low latency. Fine-tuned domain-specific models using LoRA/QLoRA, improving robustness i

ML Engineer at TVS Motor Company

October 1, 2021 - December 1, 2023

Developed vehicle telemetry anomaly detection platform for connected mobility systems, analyzing IoT sensor streams to improve incident detection speed by 28% and enhance real-time fleet monitoring. Designed scalable ETL pipelines using Azure Data Factory, PySpark, and SQL to process millions of daily events. Built and fine-tuned models (XGBoost, LightGBM, autoencoders) for predictive maintenance and anomaly detection, improving predictive accuracy by 32%. Applied NLP with BERT and TF-IDF logistic regression to service center transcripts and customer feedback, boosting issue prioritization by 42%. Containerized models with Docker and deployed on Azure Kubernetes Service for real-time inference, reducing latency by 380%. Created dashboards and experimentation frameworks to enable A/B testing and performance tracking, cutting manual reporting by 49%.