Hi! I'm Kumar Dahal

AI Apprentice, Implementing AI With Amazon ML, Extending Amazon Machine Learning, AI Architect

AI Apprentice: -

✓ Collaborated with AI engineers and developers to fit best approach and common mistakes to developing AI, and communicated the value AI that can bring to a business, and list multiple areas in with use of AI.

AI Developer: -

✓ Utilized machine learning, deep learning, reinforcement learning, as well as probabilistic techniques to achieve intelligent behavior in robotic system for automotive, healthcare, logistic, and military industries with increasing computer power and sophistication in small robotics.

AI Practitioner: -

✓ Researched and identified significant components of IIS and created a self-driving vehicle and worked with python libraries to build high-level components of a Markov decision process.

AI Architect: -

✓ • Introduced the concepts, methodologies, and tools required for effectively and efficiently incorporating AI into TI enterprise planning with stakeholders and CEO. • Visualized enterprise level planning from an AI perspective, and view projects in tactical/strategic and current, intermediate, or future state contexts. • Explored how to use an AI Maturity Model to conduct an AI Maturity Assessment of the current and future states of AI planning, and how to conduct a gap analysis between those states. Created an AI enterprise roadmap using all of the artifacts just described, plus a KPIs/Value Metrics table, and how both of these can be use as inputs to an analytics dashboard.

ML Programmer, DL Programmer, ML Engineer, ML Architect

ML Programmer: -

✓ • Built and implemented machine learning models and pipelines and managed classification and regression problems for various projects. • Used Scikit-learn to recognize the various models that can be used to manage classification and regression problems. • Implemented scitket-learn ML pipelines steps to iterative machine learning model management and associated project benefits.

DL Programmer: -

✓ Architect various layers of convolutional networks, including pooling layer, convo layer, normalization layer, and fully connected layer, and defining the concept and types of filters in convolutional networks along with their usage scenarios. Implemented computer vision, and developed CNN models from scratch for object image classification by using Python and Keras. Explored artificial neural networks (ANNs), their essential components, tools, and frameworks for their implementation in machine learning solutions. Used probability theory to enable prediction and other analytical types in ML, including the role of probability in chain rule and Bayes' rule.

ML Engineer: -

✓ Discovered business problems that can be resolved by using predictive modeling, exploratory data analytic and different types of data sets and variables. Performed predictive analytics applications to drive revenues, reduce costs, and provide a competitive advantage to businesses. Collaborated with data science and overall platform for the benefits and challenges of machine learning and application deployment. Designed automation including what Display Status is, and also the Human-Computer Collaboration automation design principle.

ML Architect: -

✓ Conceptualized and designed an applications between machine learning and deep learning (ML/DL) model to recognize challenges and strategies associated with debugging deep learning networks and algorithms. The closing exercise involves identifying the steps of ML workflow, deep learning frameworks, and strategies for debugging deep learning networks. Troubleshoot and annualized the problems with deep learning models, and build robust deep learning solutions for technical challenges of managing diversified kinds of data with ML (machine learning), and how to work with its challenges. • Explore deep learning with Keras, including how to create and use neural networks with Keras for machine learning solutions. Trained junior developers about what neural networks are and their main components, followed by an introduction to Keras and its guiding principles.

AWS, Azure, GCP

Multi-Cloud AWS: -

AWS DevOps Engineer (Professional) Certification, Master the AWS Well-Architected Framework, choosing a Cloud Platform for Developers: AWS, Azure, and GCP, Learning Amazon Web Services (AWS) for Developers, AWS Essential Training for Architects, AWS: Monitoring and Reporting, Learning AWS CloudFormation.

Multi-Cloud GCP: -

Google Cloud Architect - Cloud Design, Network Components, Web Applications & Name Resolution, Data Storage, Virtual Machine Deployment, Security, Virtual Machine Configuration, Monitoring & Logging, Programmatic Access, Identity Management, Solution Management & Testing, Troubleshooting, CLI Cloud Resource Management.

Multi-Cloud Azure: -

Azure Architect Technologies Certification, Deploy and Manage Virtual Machines, Deploy and Manage Virtual Machines, Configure and Manage Virtual Networks, Manage Identities, Implement and Manage Storage, Azure Serverless Computing, Load Balancers and Application Gateways, Implement and Manage Application Services, Docker on Azure, Developing CI/CD Solutions with Azure DevOps, Cosmos DB, Azure Essential Training for Developers, Azure Event Grid, AZ400, AZ300.

Network Infrastructure: -

NFV, SDN, SD-WAN, DPDK, MPLS, VOIP, TCP/IP, DNS, DHCP, VPN, IAS, DOCSIS. Network Architecture and design: Active Directory, Group policy, Terminal Services, Routing and Remote Access, wiring and cable terminations, Hardware based CPE (Intel and ARM).

Docker, Kubernetes, Native Tools, Microservices, Cloud Native Ecosystem, Monitoring with Prometheus, Service Mesh with Istio

Docker: -

✓ • Architect containers and Kubernetes, and build microservices architectures for reliability and resilience. • Leverage open-source tools for cloud-native application delivered and built entire network system infrastructure for seamless deployment, execution and validation. • Utilized Docker to run complex systems with millions of users and hundreds of containers. • Created Docker images, volumes, registries, linked containers, managed network and namespaces with Docker. Built and deployed application to EKS, GKE and AKS with Docker.

Kubernetes: -

✓• Migrated entire data from monolith to stable microservices architecture with robust, scalable and containerization deployment and orchestration. • Configured end-to-end evolution, key features, and components of Kubernetes, identifyed the pros and cons of the recommended installation and configuration options for Kubernetes and established the key criteria for evaluating Kubernetes solutions for enterprise workloads, including using Pods to manage multiple containers. • Additionally, install all-in-one Kubernetes using containers to run Kubernetes components as a Docker containers on a single host. • Dynamically scaled Kubernetes capability to deploy in order to scale the number of Pods in a deployment up or down and add resiliency using various commands and tools. • Configured DaemonSet patterns for communicating with the Pods in a DaemonSet, the role of Kubernetes Garbage Collector, and the resource types and resource units used in Kubernetes. • Created a deployment and scale the deployment, scale StatefulSets and make in-place updates, and created a defined Horizontal Pod Autoscaler and DaemonSet. Deployed a replicated topology with a StatefulSet controller, simulate Pod and Node downtime, and implement a case study of cascading deletion and managing orphans dependents. Configured a Kubernetes cluster to ingest logs into Elasticsearch and visualize the metrics using Kibana

Cloud Native Ecosystem: -

✓Utilized some of the tools and platform provided by Cloud Native Computing Foundation (CNCF) for management and orchestration, networking and runtime, application observability, and analysis and security. • Galvanized and trained engineers and developers on challenges that involves in adopting cloud native or cloud distributed system architectures. • Monitored cloud native with Prometheus, application logging and tracing, and tools to help with security, policy, and distribution.

DevOps Engineer, Cloud Engineer, CloudOps Engineer, Enterprise Cloud Architect

DevOps Engineer : -

✓ Designed and implemented sustainable CI/CD automation for build, test and deployment of enterprise web applications written in .NET, JavaScript and JAVA.

Cloud Engineer: -

✓ Collaboratively engaged team to focus on DevOps automation architect by creating strategies and design patterns on AWS, Azure, GCP automation across platforms.

CloudOps Engineer : -

✓ implemented and optimized multi-cloud strategy in the enterprise, the CloudOps design scenarios for managing critical architectural and operational requirement of multi-cloud strategy.

Enterprise Cloud Architect : -

✓ Designed end-to-end Cloud Architect solutions to automate and orchestrate multi-cloud applications, along with the Cloud Computing Reference Model and Cloud Governance Lifecycle, which are used for building cloud frameworks.

CCSP, CISSP, SY0-601: CompTIA Security+, DevSecOps, HashiCorp Vault, Ethical Hacker, Advance in Kali and penetration testing.

Certified Cloud Security Professional (CCSP): -

✓ Developed, maintained and managed Security Assessment and Authorization (SA&A) packages that include System Security Plans (SSP), Contingency Plans (CP), Security Impact Analysis (SIA), POA&Ms, and other relevant security documentation for existing and new systems.

Certified Information Systems Security Professional (CISSP): -

✓ Developing and maintaining information security program strategic and tactical goals and objectives and program outreach/communication plans.

SY0-601: CompTIA Security+: -

✓ Identified and access management concepts for enterprise companies to install and configure identity and access services. Implemented identity and access management controls and different common account management practices.

DevSecOps: -

✓ Provided leadership in the DevSecOps areas of Vulnerability Scanning, Certificate Management, Password Policy Management, Data Analysis of security monitoring outputs, coordination of Remediation Patching, and other daily Security and Compliance efforts.

HashiCorp Tools: -

✓ Simplified infrastructure automation by utilizing tools like Vagrant, Terraform, Packer, and Vault, you can manage virtual machines, define declarative infrastructure as code, automate machine images, and manage secrets across your infrastructure.

Ethical Hacker: -

✓ Expert ability to identify vulnerabilities in an organization's network or infrastructure, and then address the issues to prevent incidents or attacks. Performed penetration testing and gain the knowledge and skills to implement countermeasures to prevent cybersecurity attacks.

Advance in Kali: -

✓ Exploited Advanced Kali Linux system to do penetration-testing main tool, including hundreds of modules for scanning, exploitation, payloads, and post exploitation.

penetration testing: -

✓ Provide technical support to regulatory agencies, external auditors, and internal auditors, as required, to respond to audits and examinations of the Bank's control environment

Agile, Kanban, Scrum, SAFe Team, SAFe DevOps,

Agile: -

✓ helped lead agile scrum teams and ensure the highest value deliverable are implemented as help safeguard the world’s most critical infrastructure.

Kanban: -

✓ Experienced working in an iterative based development environment using Agile/SCRUM or Kanban

Scrum: -

✓ Engaged with Scrum Master and team members on a regular basis to ensure assigned features are fully executed within the prescribed Program Increment, Hold all appropriate Product Owner ceremonies as necessary (Grooming, Demo, etc.)

SAFe Team: -

✓ Lead the well-integrated team in a sAFE Agile manner across all contributing partners mentioned above (data engineers, data governance, data scientists, service designers and software engineers) to define product features, design capabilities and execute build and delivery for the committed scope and timelines

SAFe DevOps: -

✓ Worked directly with the DevOps team and Senior DevOps manager to test system integrity. Designed and implement build, deployment, and configuration management.

Tools: Git, Jenkins, Docker Architecture, Kubernetes Architecture, HashiCorp Tools, Ansible, Puppet, Chef, Red Hate, Nagios, Selenium, Salt Stack, Splunk, Angular, EKL, SoarQube, Node.js, React, .Net Core. Language: HTML, JSON, XML, CSS, JavaScript, PHP, JAVA, C++ and Python.

AWS, Azure, GCP

Multi-Cloud AWS: -

AWS DevOps Engineer (Professional) Certification, Master the AWS Well-Architected Framework, choosing a Cloud Platform for Developers: AWS, Azure, and GCP, Learning Amazon Web Services (AWS) for Developers, AWS Essential Training for Architects, AWS: Monitoring and Reporting, Learning AWS CloudFormation.

Multi-Cloud GCP: -

Google Cloud Architect - Cloud Design, Network Components, Web Applications & Name Resolution, Data Storage, Virtual Machine Deployment, Security, Virtual Machine Configuration, Monitoring & Logging, Programmatic Access, Identity Management, Solution Management & Testing, Troubleshooting, CLI Cloud Resource Management.

Multi-Cloud Azure: -

Azure Architect Technologies Certification, Deploy and Manage Virtual Machines, Deploy and Manage Virtual Machines, Configure and Manage Virtual Networks, Manage Identities, Implement and Manage Storage, Azure Serverless Computing, Load Balancers and Application Gateways, Implement and Manage Application Services, Docker on Azure, Developing CI/CD Solutions with Azure DevOps, Cosmos DB, Azure Essential Training for Developers, Azure Event Grid, AZ400, AZ300.

Network Infrastructure: -

NFV, SDN, SD-WAN, DPDK, MPLS, VOIP, TCP/IP, DNS, DHCP, VPN, IAS, DOCSIS. Network Architecture and design: Active Directory, Group policy, Terminal Services, Routing and Remote Access, wiring and cable terminations, Hardware based CPE (Intel and ARM).