mlops engineer

VARUN MURTHY MOKARALA

Dallas, Texas +1-469-***-**** *****.******@*****.**

Professional Summary

I’m a certified GCP Professional and Senior Machine Learning Engineer with a passion for solving complex problems. With 7 years of solid experience in MLOps, infrastructure development, automation, and AI research, I’ve built and scaled solutions using Python in fast-paced, data-driven environments. I’m known for being tenacious, collaborative, and highly organized, and I thrive in teams where I can learn, contribute, and make a real impact.

Experience

Senior MlOps Engineer Sep 2022 - Present

Booz Allen Hamilton, Dallas, Tx

Tools and Technologies: LLMs, vLLM, RAG Pipeline, Databricks, Generative AI, MLflow, Azure Container Registry, AWS ECR, Google Container Registry (GCR), Azure Kubernetes Service (AKS), Amazon EKS, Google Kubernetes Engine (GKE), Blob Storage, S3, Google Cloud Storage, Vertex AI, SageMaker, Azure ML, Kubeflow, Ingress, TensorFlow Extended, Kubernetes, Istio, AWS App Mesh, APISIX, FastAPI, AWS, Azure, GCP, Docker, Prometheus, Grafana, Secrets, mTLS, Cloud Functions, Lambda, Cloud Storage, Cloud Pub/Sub, CloudWatch, Azure Monitor, Cloud Monitoring, Terraform, Argo CD, Keycloak, Streamlit, GitLab CI/CD, GitLab Runner, Python

Roles and Responsibilities:

●Supported Databricks platform operations including workspace setup, access control configuration (RBAC & Unity Catalog), cluster management, and integration with Azure AD for seamless authentication and governance.

●Provisioned and maintained Azure resources including Azure Databricks, Storage Accounts, Virtual Networks, and Managed Identities using Terraform, enabling fully automated infrastructure deployment and versioning

●Developed and fine-tuned large language models (LLMs) using Lora and Generative AI models for various applications, enhancing natural language processing capabilities and model performance.

●Worked on the SAGE project, utilizing RAG (Retrieval-Augmented Generation) Pipeline, LLM Models, GenAI, and Prompt Engineering techniques to enhance the system's capabilities.

●Technical project lead, supporting teammates, proposing projects, implementation architectures, presentation of the products across multi-cloud MLOps initiatives.

●Designed and implemented fully automated MLOps pipelines using AWS SageMaker, Azure ML, and Google Cloud AI Platform, ensuring efficient model training, deployment, and monitoring across cloud platforms

●Feature Engineering & Data Preparation leveraging both visual and Python-based transformations

●Orchestrated data and ML pipelines using TensorFlow Extended and MLflow, leveraging container registries (ECR, ACR, GCR) for containerization to ensure reproducibility and scalability across cloud environments.

●Deployed and managed ML infrastructure including Milvus, Databricks, Spark, PostgreSQL, Kubeflow, Streamlit, ArgoCD, Keycloak, Prometheus, and Loki on Kubernetes clusters across AWS EKS, Azure AKS, and Google GKE using Terraform for infrastructure as code.

●Implemented service mesh solutions (Istio, AWS App Mesh) across multi-cloud Kubernetes deployments, enhancing microservices communication, security, and observability.

●Configured traffic management, fault injection, and retries to improve application resilience across cloud platforms..

●Developed and managed microservices using FastAPI and APISIX, integrating with cloud-native services from AWS, Azure, and GCP for efficient and scalable application architecture.

●Implemented cloud-specific ingress controllers (ALB Ingress, Azure Application Gateway,

Google Cloud Load Balancing) to manage external access to services deployed in multi-cloud Kubernetes clusters.

●Applied Role-Based Access Control (RBAC), Network Policies, and Pod Security Policies to secure Kubernetes clusters across AWS, Azure, and GCP environments

Set up comprehensive monitoring and logging solutions using Prometheus, Grafana, and ELK Stack, integrated with cloud-native monitoring services (CloudWatch, Azure Monitor, Cloud Monitoring) to ensure visibility into system performance.

ML Engineer II- (MlOps-GCP) Nov 2022-Aug 2023

PNC Financial Services., Tampa, FL

Tools and Technologies: Demand Forecasting, Python, GCP Composer (Data and ML Pipeline in Dags), Airflow, BigQuery, SQL, Kubeflow Pipeline, Vertex AI, TensorFlow Extended, Unit Testing, Git Actions, Git CI/CD, Docker, ADO, Cloud Functions, Cloud Storage, Cloud Pub/Sub

Roles and Responsibilities:

●Designing and implementing AI algorithms for computer vision and voice recognition, ensuring high accuracy and reliability in multimodal data processing.

●Collaborating with cross-functional teams to integrate AI capabilities into the HIP platform, ensuring seamless interoperability and functionality.

●Automated Descriptive Analysis Tasks: Developed automation for descriptive analysis tasks using Python and GCP Composer Dags, incorporating unit testing to ensure reliability and accuracy.

●ML Pipeline Automation: Designed and implemented a fully automated ML pipeline using Kubeflow, orchestrated with TensorFlow Extended. Containerized ML components using Docker and utilized BigQuery as the data warehouse for seamless data management and integration.

●Pipeline Orchestration: Orchestrated data and ML pipelines with Airflow and GCP Composer as managed services for demand forecasting and other ML tasks. Leveraged BigQuery for data warehousing, ensuring efficient data flow and processing within the pipeline.

●Model Serving and Monitoring: Implemented model serving and monitoring to detect model drift and data drift, ensuring the models' performance and accuracy remain optimal over time.

●Serverless Functions: Utilized GCP Cloud Functions for event-driven data processing and orchestration tasks, enhancing pipeline flexibility and scalability.

●Cloud Storage Integration: Integrated Cloud Storage for scalable and durable storage of datasets and model artifacts, ensuring high availability and accessibility.

Senior Data Scientist (MlOps-Azure) Jul 2020 to Mar 2021

Charter Communications, Tampa, FL

Tools and Technologies: Deep Learning, Statistics, Python, Flask, Azure ML Flow, Azure Data Factory, Azure DevOps, Computer Vision, OpenCV, ETL, Mlflow, Snowflake, Azure Databricks, Docker, Azure Databases, Gitlab CI pipelines, Azure CI/CD pipelines.

Roles and Responsibilities:

●Developed and automated deep learning 1D CNN model pipelines to detect anomalies in wind turbine gearboxes and main bearing failures. Implemented these pipelines in Azure CI/CD with Docker containers for seamless integration and deployment.

●Created automated data preprocessing tasks using Python scripts to streamline data preparation workflows.

●Developed a Computer Vision YOLO model to detect defects in wind turbine blades. Deployed the model in the cloud, integrated with drones, and utilized Mlflow in the train.py script for model logging and monitoring.

●Developed and deployed machine learning and time series models to detect main bearing failures from oil sample data, enhancing predictive maintenance capabilities

●Implemented mathematical formulas such as Fast Fourier Transform and Hilbert Transform for amplitude demodulation in DFIG-based turbines to inspect rotor failures.

●Developed a comprehensive wind turbine application (Suzlatics) for predictive analysis of main bearing temperature. Implemented graphical representations in Python for data visualization.

●Simulated the Normal Turbulence Model (NTM) in Python to analyze extreme wind loads or external loads on shafts using incoming data from the SCADA system.

Data Analyst Intern Jan 2019 – May 2020

Washkewicz College of Engineering, Cleveland, Ohio

Tools and Technologies: SQL, SSRS, SSIS, SQL Profiler, Tableau, QlikView, ETL, Anomaly detection.

Roles and Responsibilities:

●Responsible for gathering requirements from Business Analyst and Operational Analyst and identifying the data sources required for the request.

●Worked closely with a data architect to review all the conceptual, logical and physical database design models with respect to functions, definition, maintenance review and support data analysis, Data quality and ETL design that feeds the logical data models.

●Maintained and developed complex SQL queries, stored procedures, views, functions, and reports that qualify customer requirements using SQL Server 2012.

●Creating automated anomaly detection systems and constant tracking of its performance.

●Support Sales and Engagement's management planning and decision making on sales incentives.

●Used statistical analysis, simulations, predictive modelling to analyze information and develop practical solutions to business problems.

●Extending the company's data with third-party sources of information when needed.

●Précised development of several types of sub-reports, drill down reports, summary reports, parameterized reports, and ad-hoc reports using SSRS through mailing server subscriptions &SharePoint server.

●Generated ad-hoc reports using Crystal Reports 9 and SQL Server Reporting Services (SSRS).

●Developed the reports and visualizations based on the insights mainly using Tableau and dashboards for the company insight teams.

Machine learning Engineer Nov 2017 to Nov 2018

Smart Bridge IoT Solutions., Hyderabad, India

Tools and Technologies: Forecasting, Time Series Analysis, Computer Vision, Deep Learning, OpenCV, AWS Forecasting, MLOps, ETL, Docker, Azure Data Factory, Azure Databricks, Azure CI/CD Pipeline

Roles and Responsibilities:

●Architected and deployed end-to-end MLOps solutions on Azure Machine Learning, implementing automated ML pipelines for forecasting automotive parts demand using Facebook Prophet for Volkswagen Portugal

●Designed and implemented Azure DevOps CI/CD pipelines with automated model training, testing, and deployment using Azure ML SDK and Azure Pipelines YAML

●Set up comprehensive model monitoring and experiment tracking using Azure ML Studio and MLflow, enabling real-time performance monitoring and model governance

●Built and deployed Computer Vision models on Azure Kubernetes Service (AKS) using YOLOv5 for real-time defect detection in industrial conveyor systems, with Azure Container Registry for image management

●Implemented Azure IoT Edge solutions for deploying SSD object detection models with OpenCV at the edge for steel plate quality control, utilizing Azure IoT Hub for device management

●Created production-grade LSTM models for industrial forecasting, deployed through Azure Machine Learning endpoints and integrated with Azure Synapse Analytics for data processing

●Implemented MLOps best practices including model versioning, A/B testing, and automated retraining using Azure ML pipelines and Azure Functions for event-driven architecture

Data analyst May 2017 to Nov 2017

IETE Pvt. Ltd., Hyderabad, India

Roles and Responsibilities:

●Collected, interpreted, and analyzed large datasets to derive actionable insights for business decision making.

●Performed predictive analytics on telecommunication data to forecast trends and improve customer retention strategies.

●Implemented various machine learning concepts, including data processing, supervised learning, and unsupervised learning, to enhance data analysis capabilities.

●Built a conversational chatbot to provide elementary information to telecommunication customers, utilizing NLP techniques for effective communication.

●Employed various statistical sampling methods to perform sampling on datasets, ensuring the accuracy and reliability of analysis results.

●Developed mathematical and statistical functions in Python to support complex data analysis tasks.

●Fetched data using MySQL queries from AWS Redshift, ensuring seamless data retrieval for analysis.

Certifications

Google Cloud Professional Cloud Architect Issued by Google Cloud

GCP Architect, Cloud Security, Storage & Databases, GKE,, Networking in Cloud

Google Cloud Professional Machine Learning Engineer Issued by Google Cloud AutoML, BigQuery ML, Cloud Storage, ML APIs, ML Ops, Responsible AI, Scalability, Vertex AI Feb 2025

Core Qualifications

●LLMS

●Generative Al

●MLOps

●Vertex Al

●Kubeflow

●Ingress

●TensorFlow Extended

●Kubernetes

●Istio

●Computer Vision

●OpenCV

●NLP

●Deep NLP

●Machine Learning

●Data Science

●Voice Analysis

●APISIX

●FastAPI

●AWS

●Azure

●GCP

●Docker

●Prometheus

●Grafana

●ELK Stack

●RBAC

●Network Policies

●ConfigMaps

●Secrets

●mTLS

●Cloud Functions

●Cloud Storage

●Cloud Pub/Sub

●Terraform

●Demand Forecasting

●Python

●GCP Composer

●Airflow

●BigQuery

●SQL

●Flask

●Azure ML Flow

●Azure Data Factory

●Azure DevOps

●ETL

●Snowflake

●Azure Databricks

●Forecasting

●Time Series Analysis

Education

Bachelor’s in computer science from JNTU India.

Master’s in computer science from Cleveland State University.

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