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

I'm Pavan Kumar Kollipara, a results-driven Machine Learning Engineer with 6+ years of experience designing, training, and deploying scalable ML and Generative AI systems on AWS, supporting enterprise-scale data pipelines and intelligent automation across multiple domains. I’ve built 50+ distributed models for prediction, recommendation, and classification on multi-terabyte datasets, and I’m proficient with PyTorch, TensorFlow, XGBoost, and SageMaker. I’ve led MLOps automation using MLflow, Airflow, GitHub Actions, ArgoCD, and Spinnaker, automating production pipelines for training, deployment, and monitoring. I also work with LLMs and RAG pipelines using LangChain, LangFuse, and Hugging Face to enable retrieval-based responses and scalable contextual reasoning. Recently I’ve focused on building retrieval workflows, agentic reasoning systems, and multi-GPU production workloads, while collaborating with cross-functional teams to translate research into reliable, scalable solutions. I enjoy mentoring teammates, driving practical ML adoption, and delivering value through end-to-end system design, monitoring, and continuous improvement.…I'm Pavan Kumar Kollipara, a results-driven Machine Learning Engineer with 6+ years of experience designing, training, and deploying scalable ML and Generative AI systems on AWS, supporting enterprise-scale data pipelines and intelligent automation across multiple domains. I’ve built 50+ distributed models for prediction, recommendation, and classification on multi-terabyte datasets, and I’m proficient with PyTorch, TensorFlow, XGBoost, and SageMaker. I’ve led MLOps automation using MLflow, Airflow, GitHub Actions, ArgoCD, and Spinnaker, automating production pipelines for training, deployment, and monitoring. I also work with LLMs and RAG pipelines using LangChain, LangFuse, and Hugging Face to enable retrieval-based responses and scalable contextual reasoning. Recently I’ve focused on building retrieval workflows, agentic reasoning systems, and multi-GPU production workloads, while collaborating with cross-functional teams to translate research into reliable, scalable solutions. I enjoy mentoring teammates, driving practical ML adoption, and delivering value through end-to-end system design, monitoring, and continuous improvement.

Pavan Kumar Kollipara

Data Scientist, AI Engineer, AI Developer, +4





I'm Pavan Kumar Kollipara, a results-driven Machine Learning Engineer with 6+ years of experience designing, training, and deploying scalable ML and Generative AI systems on AWS, supporting enterprise-scale data pipelines and intelligent automation across multiple domains. I’ve built 50+ distributed models for prediction, recommendation, and classification on multi-terabyte datasets, and I’m proficient with PyTorch, TensorFlow, XGBoost, and SageMaker. I’ve led MLOps automation using MLflow, Airflow, GitHub Actions, ArgoCD, and Spinnaker, automating production pipelines for training, deployment, and monitoring. I also work with LLMs and RAG pipelines using LangChain, LangFuse, and Hugging Face to enable retrieval-based responses and scalable contextual reasoning. Recently I’ve focused on building retrieval workflows, agentic reasoning systems, and multi-GPU production workloads, while collaborating with cross-functional teams to translate research into reliable, scalable solutions. I enjoy mentoring teammates, driving practical ML adoption, and delivering value through end-to-end system design, monitoring, and continuous improvement.…I'm Pavan Kumar Kollipara, a results-driven Machine Learning Engineer with 6+ years of experience designing, training, and deploying scalable ML and Generative AI systems on AWS, supporting enterprise-scale data pipelines and intelligent automation across multiple domains. I’ve built 50+ distributed models for prediction, recommendation, and classification on multi-terabyte datasets, and I’m proficient with PyTorch, TensorFlow, XGBoost, and SageMaker. I’ve led MLOps automation using MLflow, Airflow, GitHub Actions, ArgoCD, and Spinnaker, automating production pipelines for training, deployment, and monitoring. I also work with LLMs and RAG pipelines using LangChain, LangFuse, and Hugging Face to enable retrieval-based responses and scalable contextual reasoning. Recently I’ve focused on building retrieval workflows, agentic reasoning systems, and multi-GPU production workloads, while collaborating with cross-functional teams to translate research into reliable, scalable solutions. I enjoy mentoring teammates, driving practical ML adoption, and delivering value through end-to-end system design, monitoring, and continuous improvement.

Available to hire

I’m Pavan Kumar Kollipara, a results-driven Machine Learning Engineer with 6+ years of experience designing, training, and deploying scalable ML and Generative AI systems on AWS, supporting enterprise-scale data pipelines and intelligent automation across multiple domains. I’ve built 50+ distributed models for prediction, recommendation, and classification on multi-terabyte datasets, and I’m proficient with PyTorch, TensorFlow, XGBoost, and SageMaker. I’ve led MLOps automation using MLflow, Airflow, GitHub Actions, ArgoCD, and Spinnaker, automating production pipelines for training, deployment, and monitoring. I also work with LLMs and RAG pipelines using LangChain, LangFuse, and Hugging Face to enable retrieval-based responses and scalable contextual reasoning.

Recently I’ve focused on building retrieval workflows, agentic reasoning systems, and multi-GPU production workloads, while collaborating with cross-functional teams to translate research into reliable, scalable solutions. I enjoy mentoring teammates, driving practical ML adoption, and delivering value through end-to-end system design, monitoring, and continuous improvement.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

ML Research Assistant at UMBC

August 1, 2025 - November 18, 2025

Developed and fine-tuned LLMs using Hugging Face, LangChain, and OpenAI APIs on AWS SageMaker and EMR, integrating PySpark ETL pipelines to improve sensor data inference on 1M+ records. Built and optimized RAG pipelines with LangChain, Pinecone, and LangGraph, analyzing 500GB+ of sensor logs for faster and more accurate contextual responses. Implemented MLOps workflows automating 200+ model runs for tracking, drift detection, and reproducibility. Deployed agentic AI frameworks leveraging LangGraph, MCP, and MLflow, automating data prep, tuning, and scheduling for 30K+ records per run on multi-GPU clusters.

ML Engineer at Wipro Limited

August 1, 2023 - August 1, 2023

Developed and deployed 8+ ML models for fraud detection, traffic classification, recommendations, and engraving using PyTorch, Scikit-learn, and AWS SageMaker, automating ETL in Airflow, Glue, and Kafka for 100K+ daily transactions. Built real-time traffic filtering models using supervised and anomaly detection, blocking 250K+ bot requests daily across 15 global storefronts with AWS Step Functions and Lambda workflows. Collaborated on CNN-based text-to-engraving model on multi-GPU clusters, processing 50K+ text inputs and improving accuracy across 5K+ SKUs. Streamlined MLOps pipelines with MLflow, Prometheus, and Grafana, enabling 250+ retraining cycles and versioned deployments via SageMaker Pipelines.

ML Engineer at Cipla

September 1, 2021 - September 1, 2021

DevOps Engineer at Trigent

May 1, 2019 - May 1, 2019

Developed infrastructure automation using Python, Shell scripting, CloudFormation, Ansible, and AWS SSM, provisioning 500+ AWS instances across 8 environments. Built CI/CD pipelines with GitHub, Jenkins, GitLab CI, SonarQube, and JFrog, automating build and deployment for 30+ applications. Managed AWS infrastructure with EC2, ECS, EKS, S3, RDS, Lambda, and CloudFormation, implementing multi-region replication and HA/DR solutions across 5 regions. Containerized and orchestrated 40+ microservices using Docker and Kubernetes, integrating AWS ElastiCache and Hazelcast for distributed caching, reducing API response time to 300 ms and supporting 10K+ concurrent daily users across production clusters.

ML Engineer at Wipro Limited

September 1, 2021 - August 1, 2023

Developed and deployed 8+ ML models for fraud detection, traffic classification, recommendations, and engraving using PyTorch, Scikit-learn, and AWS SageMaker, automating ETL in Airflow, Glue, and Kafka for 100K+ daily transactions. Built real-time traffic filtering models using supervised and anomaly detection, blocking 250K+ bot requests daily across 15 global storefronts with AWS Step Functions and Lambda workflows. Collaborated on a CNN-based text-to-engraving model on multi-GPU (NVIDIA A100) clusters, processing 50K+ text inputs and improving engraving accuracy across 5K+ SKUs. Streamlined MLOps pipelines with MLflow, Prometheus, and Grafana, automating 250+ retraining cycles and enabling versioned deployments via SageMaker Pipelines. Optimized deployments using FastAPI, Flask, Istio, and Cilium eBPF, achieving zero downtime across 15+ Apple Store environments; built 5+ Go-based CLI tools for automation. Enhanced scalability with Terraform, Helm, and AWS SDK, cutting environment s

ML Engineer at Cipla

June 1, 2019 - September 1, 2021

Architected end-to-end ML pipelines on AWS SageMaker using PyTorch, XGBoost, and Docker, training 10+ production models and reducing latency across 1M+ daily predictions. Engineered ETL and data warehousing pipelines with PySpark, AWS EMR, and Redshift, processing 50M+ clinical records daily with automated validation and schema evolution. Automated retraining and tuning workflows with Airflow DAGs and MLflow, managing 300+ model runs across 5 teams and cutting manual effort by 40+ hours monthly. Deployed models using Canary, A/B, and blue-green strategies via CI/CD pipelines, maintaining zero downtime across 12 environments and serving 1M+ predictions daily. Implemented model monitoring and drift detection with SageMaker Model Monitor and CloudWatch, tracking feature drift and accuracy metrics for 15+ endpoints. Built Tableau and Power BI dashboards visualizing model metrics, drift, and ETL throughput, enabling 50+ stakeholders to monitor and improve R&D performance.

DevOps Engineer at Trigent

May 1, 2017 - May 1, 2019