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

I’m an AI/ML Engineer focused on building production-grade GenAI and graph-enhanced systems for high-scale, real-time decisioning. I’ve delivered fraud detection and transaction risk platforms that combine knowledge graphs, GraphRAG, and predictive modeling to improve accuracy, alert prioritization, and operational reliability across enterprise environments. I also build scalable streaming and distributed ML pipelines end-to-end—from experimentation to deployment—using AWS, Spark, Kafka, and Kubernetes. Along with hands-on MLOps (MLflow, CI/CD, monitoring, drift detection), I’ve developed LLM fine-tuning workflows (LoRA/QLoRA/PEFT) and AI agents for AML, fraud, and credit workflows, ensuring models perform reliably in secure production settings.…I’m an AI/ML Engineer focused on building production-grade GenAI and graph-enhanced systems for high-scale, real-time decisioning. I’ve delivered fraud detection and transaction risk platforms that combine knowledge graphs, GraphRAG, and predictive modeling to improve accuracy, alert prioritization, and operational reliability across enterprise environments. I also build scalable streaming and distributed ML pipelines end-to-end—from experimentation to deployment—using AWS, Spark, Kafka, and Kubernetes. Along with hands-on MLOps (MLflow, CI/CD, monitoring, drift detection), I’ve developed LLM fine-tuning workflows (LoRA/QLoRA/PEFT) and AI agents for AML, fraud, and credit workflows, ensuring models perform reliably in secure production settings.

Chandra Pavan Kumar

AI Engineer, Data Engineer, Software Engineer, +4





I’m an AI/ML Engineer focused on building production-grade GenAI and graph-enhanced systems for high-scale, real-time decisioning. I’ve delivered fraud detection and transaction risk platforms that combine knowledge graphs, GraphRAG, and predictive modeling to improve accuracy, alert prioritization, and operational reliability across enterprise environments. I also build scalable streaming and distributed ML pipelines end-to-end—from experimentation to deployment—using AWS, Spark, Kafka, and Kubernetes. Along with hands-on MLOps (MLflow, CI/CD, monitoring, drift detection), I’ve developed LLM fine-tuning workflows (LoRA/QLoRA/PEFT) and AI agents for AML, fraud, and credit workflows, ensuring models perform reliably in secure production settings.…I’m an AI/ML Engineer focused on building production-grade GenAI and graph-enhanced systems for high-scale, real-time decisioning. I’ve delivered fraud detection and transaction risk platforms that combine knowledge graphs, GraphRAG, and predictive modeling to improve accuracy, alert prioritization, and operational reliability across enterprise environments. I also build scalable streaming and distributed ML pipelines end-to-end—from experimentation to deployment—using AWS, Spark, Kafka, and Kubernetes. Along with hands-on MLOps (MLflow, CI/CD, monitoring, drift detection), I’ve developed LLM fine-tuning workflows (LoRA/QLoRA/PEFT) and AI agents for AML, fraud, and credit workflows, ensuring models perform reliably in secure production settings.

Available to hire

I’m an AI/ML Engineer focused on building production-grade GenAI and graph-enhanced systems for high-scale, real-time decisioning. I’ve delivered fraud detection and transaction risk platforms that combine knowledge graphs, GraphRAG, and predictive modeling to improve accuracy, alert prioritization, and operational reliability across enterprise environments.

I also build scalable streaming and distributed ML pipelines end-to-end—from experimentation to deployment—using AWS, Spark, Kafka, and Kubernetes. Along with hands-on MLOps (MLflow, CI/CD, monitoring, drift detection), I’ve developed LLM fine-tuning workflows (LoRA/QLoRA/PEFT) and AI agents for AML, fraud, and credit workflows, ensuring models perform reliably in secure production settings.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Beginner

Work Experience

AI/ML Engineer at U.S Bank

June 1, 2024 - Present

Built an AI-based banking risk platform using graph analytics, knowledge graphs, and predictive models for fraud detection and transaction monitoring, improving fraud identification accuracy by 28%. Developed real-time data pipelines with AWS Glue, Kinesis, Spark, Iceberg, and Redshift to process 1M+ banking transactions for faster AI-driven risk analysis. Created AI agents with LangGraph, CrewAI, AutoGen, and MCP to support fraud/AML/credit workflows, reducing investigation time by 9% using automated case analysis and intelligent retrieval. Implemented GraphRAG with Neo4j vector databases and LLMs to provide risk insights and improve alert prioritization, increasing investigator productivity by 15%. Fine-tuned Llama and Mistral models with QLoRA/PEFT and hyperparameter tuning to optimize performance for risk prediction and fraud/credit tasks. Used MLflow, Kubeflow, Docker, Kubernetes, Terraform, and AWS services to manage deployment, monitoring, and governance, improving production mo

AI/ML Engineer at U.S. Bank

June 1, 2024 - Present

Built an AI-based banking risk platform using graph analytics and knowledge graphs to support fraud detection and transaction monitoring, improving fraud identification accuracy by 28%. Developed real-time data pipelines with AWS Glue, Kinesis, Spark, Iceberg, and Redshift to process 1M+ banking transactions and customer activity data for faster risk analysis. Created AI agents with LangGraph, CrewAI, AutoGen, and MCP to streamline fraud/AML/credit workflows and reduce investigation time by 9% through automated case analysis and information retrieval. Applied GraphRAG with Neo4j vector databases and language models to generate risk insights, improve alert prioritization, and increase investigator productivity by 15%. Fine-tuned Llama and Mistral models using QLoRA/PEFT and hyperparameter tuning, and used MLflow, Kubeflow, Docker, Kubernetes, Terraform, and AWS services to improve production model reliability by 22%.

ML Engineer at Uber Technologies

June 1, 2021 - July 1, 2023

Designed a real-time rider-driver matching system using embedding models and H3 geospatial indexing to improve dispatch accuracy and reduce pickup latency by 18% at Uber scale. Built streaming feature pipelines with Apache Kafka and Apache Flink for real-time ETA prediction and dynamic pricing signals, improving surge pricing accuracy by 22%. Enhanced marketplace ranking and driver incentive models using gradient-boosted learning, improving trip conversion rate by 16% through large-scale A/B testing (reported production metrics included Precision@K=0.87, Recall=0.81, F1=0.84, ROC-AUC=0.90). Implemented scalable ingestion and transformation pipelines for marketplace systems, standardizing event-driven architecture and improving indexing throughput by 21%. Deployed production inference services with Docker and Kubernetes and CI/CD pipelines, supporting real-time predictions for 2.1M+ marketplace records. Refactored legacy analytics into microservices and built Python evaluation framework