Devops Engineer Machine Learning

K HASINI

DevOps Engineer

Ph: 314-***-****

*******@*****.***

• 4+ years of IT experience as a DevOps Engineer specializing in cloud infrastructure automation and management across AWS and Azure environments.

• Proficient in managing and automating infrastructure using Infrastructure as Code (IaC) tools such as Terraform, CloudFormation, and ARM Templates, with strong expertise in containerization and orchestration technologies like Docker and Kubernetes.

• Adept at configuring and managing CI/CD pipelines, continuous integration, and continuous delivery solutions utilizing tools like Jenkins, Azure DevOps, and GitLab.

• Solid background in managing and automating cloud infrastructure deployments, ensuring high availability, security, and fault tolerance across multiple cloud platforms (AWS and Azure).

• Expertise in integrating monitoring solutions (Prometheus, Grafana, CloudWatch, and Azure Monitor) to maintain system health and availability, ensuring efficient incident management. Technical Proficiencies

Cloud: AWS Microsoft Azure

CI/CD and Automation tools: Jenkins Azure DevOps Gitlab Terraform Ansible CloudFormation Version control tools: GIT GitHub Bitbucket Azure Repos Gitlab Other Tools: Helm Maven Nexus Jira Slack

Monitoring and logging tools: Prometheus Grafana Splunk ELK Stack CloudWatch Azure Monitor Containerization/Orchestration Tools: Kubernetes Docker Azure Container Instances (ACI) Scripting: Python PowerShell Bash YAML JSON Database: AWS RDS MySQL PostgreSQL MongoDB Cassandra Application Servers: IIS JBoss Apache Tomcat Web Sphere Web Logic Web Servers: Apache HTTP Nginx Apache Tomcat

Operating System: Linux Windows MacOS

Machine Learning and Data Pipelines: TensorFlow PyTorch Scikit-Learn Apache Airflow Data Pipeline APIs: RESTful APIs Flask Fast API

Career Experience

DevOps Engineer, Tecton, Sunnyvale, CA January 2023 – Present Collaborated with cross-functional teams to gather system requirements and define project objectives for the efficient deployment and operation of machine learning models within a DevOps framework. Simulated benchmark events to stress-test infrastructure and monitor system performance under varying user loads across CI/CD pipelines.

• Implemented robust CI/CD pipelines using tools like GitHub Actions, Azure DevOps, and Jenkins to automate the deployment of ML models, significantly accelerating the delivery cycle and ensuring reproducible builds across environments.

• Integrated Docker and Kubernetes for containerization and orchestration, enabling consistent deployment of sentiment analysis models such as BERT and OpenAI’s GPT-based APIs, while ensuring high availability and fault tolerance.

• Achieved remarkable scalability and performance through zero-copy deployment, supported by container-native storage solutions and optimized for Kubernetes-based workloads, enabling the system to handle over 170 concurrent chat sessions with minimal latency or timeouts.

• Developed comprehensive observability dashboards using Grafana, Prometheus, and Azure Monitor, facilitating real-time tracking of model performance, system health, and service-level objectives.

• Employed Terraform and Helm for Infrastructure-as-Code (IaC) to provision cloud resources and manage Kubernetes deployments in a scalable, consistent, and version controlled manner.

• Streamlined MLOps workflows using MLflow and Azure Machine Learning, integrating model versioning, experiment tracking, and automated retraining pipelines into the DevOps lifecycle.

• Leveraged Databricks for distributed data processing and scalable NLP model training, aligning data engineering workflows with the overall DevOps strategy.

• Reduced inference latency by utilizing GPU-accelerated VMs (Tesla T4) across multiple clusters, managed with Kubernetes and optimized through Horizontal Pod Autoscaling and Node Affinity rules. DevOps Engineer, Deep Vision, Hyderabad, India May 2021- July 2022 Streamlined project management and cross-functional coordination by maintaining up-to-date task tracking and sprint progress using Jira, enhancing team transparency and Agile efficiency. Led end-to-end implementation of CI/CD workflows, automated testing, and performance evaluations, aligning software delivery with DevOps best practices. Documented Agile processes, deployment pipelines, and automation strategies to improve knowledge transfer and ensure operational consistency.

• Developed a low-code/no-code self-service platform for internal teams to deploy and manage applications and services via REST APIs, accelerating development cycles and reducing operational dependencies.

• Architected and deployed microservices using Flask and FastAPI, implementing RESTful APIs in an event-driven architecture for modularity, scalability, and high availability.

• Built a robust DevSecOps platform integrating automated security checks, infrastructure policy enforcement, and CI/CD pipelines to support both batch and real-time processing with minimal manual overhead in Pre-Prod and Production environments.

• Utilized Azure DevOps, Azure Data Factory, Data Lake, and Synapse Analytics to establish scalable data pipelines, load balancing, and deployment automation, supported by Git for source control and release management.

• Integrated various data sources and services using Jenkins for CI/CD automation, deploying workloads into Azure Kubernetes Service (AKS) and implementing Infrastructure as Code (IaC) practices using Terraform and Helm.

• Deployed a scalable system with auto-scaling and CPU throttling, optimizing cost and compute utilization—achieving a 75% reduction in infrastructure spend through dynamic resource allocation.

• Developed real-time observability dashboards with Tableau, New Relic, and Datadog, enabling live monitoring of application health, deployment metrics, system KPIs, and business performance for stakeholders. DevOps Engineer, Global Logic, Hyderabad, India July 2020 – April 2021

• Led AWS infrastructure automation using Terraform and CloudFormation, enabling highly available, secure, and scalable deployments across production environments.

• Developed and deployed containerized applications using Docker and orchestrated them with Amazon EKS, improving resource efficiency by 25% through optimized cluster configurations.

• Built and maintained CI/CD pipelines using Jenkins and GitLab, streamlining deployment workflows and reducing release cycle times.

• Worked with key AWS services including EC2, RDS, Lambda, S3, and SNS/SQS to design resilient, event-driven applications in a multi-account setup.

• Integrated Prometheus and Grafana with AWS workloads to monitor service health and performance, reducing downtime by 20% through real-time alerting and visualization.

• Implemented Auto Scaling Groups and EKS Cluster Autoscaler to automate resource scaling and optimize cloud costs across environments.

• Collaborated with security and compliance teams to define and enforce IAM roles, policies, and Secrets Manager usage, ensuring secure infrastructure deployments aligned with organizational standards. Intern, Business Artificial Intelligence Machine, Vishakhapatnam, India January 2020- July 2020 Project: Real Assistant Framework for Education Sector (Python & AWS)

• Designed and developed a theme-based Real Assistant Framework using Python and AWS, tailored for AI-driven solutions in the education domain.

• Created promotional banners, mailers, and digital assets aligned with design briefs and campaign specifications to support product marketing initiatives.

• Applied cloud services to deploy and support framework functionalities, demonstrating knowledge in cloud computing.

• Collaborated with team members to understand product vision and translated it into creative, user-friendly digital designs.

• Demonstrated strong UI/UX design sensibility, contributing to the visual identity of the AI research product campaign.

• Showed a proactive, self-driven approach to learning and problem-solving, completing all assigned tasks and deliverables on schedule.

Education

Master of Science in Computer Science, Saint Louis University, St Louis, Missouri May 2024 Bachelor of Technology in Computer Science and Engineering, K L University, India June 2021 Project: Smart Prediction of Clinical Disease using AI

• Led the application of supervised algorithms like Random Forest and K-Nearest Neighbors, enhancing diagnostic processes by Combining vast and diverse medical datasets, providing healthcare professionals with a powerful tool.

• Contributed to procedures capable of classifying diseases such as HIV, lung cancer, Covid-19, and thyroid disorders, increasing early detection rates by 30%, significantly advancing disease classification, reducing misdiagnosis rates by 18%. Certifications: An Introduction to Interactive Programming in Python and Principle of Computing – Coursera

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