Sai Vignesh Kumar
AWS Engineer
4years of work
6years in DevOps
4years with AWS
6repositories checked by code analysis
Overview
Data-centric ML practitioner (junior) focusing on exploratory network traffic analysis and anomaly detection. The strongest proven skill is rapid prototyping of data ingestion and preprocessing with pandas and applying scikit-learn IsolationForest, as shown in Network_Traffic_Anomaly_Detection_Project.ipynb. There is little or no evidence of reproducible experiments, evaluation rigor, production deployment, MLOps or production-grade AWS integration in the analyzed human-authored code.
Timeline
DevSecOps Intern
·
Junior
Saayam for All Foundation
·
Internship
Maintained CI/CD workflows using Jenkins and GitHub Actions, troubleshooting integration and release failures for a nonprofit platform. Reviewed pipeline output and application logs to resolve non-production deployment issues, including correcting IAM and configuration. Automated repeatable infrastructure and deployment steps with Bash and Pulumi, following least-privilege and secure configuration practices.
Jenkins
GitHub Actions
Bash
Pulumi
IAM
Least Privilege
University of Illinois at Springfield
Master's Degree ·
Computer Science
Sathyabama Institute of Science and Technology
Bachelor's Degree ·
Computer Science
Networking Intern
·
Junior
Besant Technologies
·
Internship
Configured Cisco lab environments including routers and switches, implementing VLANs and dynamic routing protocols such as OSPF and RIP. Performed troubleshooting of TCP/IP and Linux networking issues using Wireshark, routing table checks, and DNS tools. Implemented ACL and firewall controls, documenting configurations and validating permitted traffic flows.
Wireshark
TCP/IP
DNS
VLAN
OSPF
Cloud Computing Intern
·
Junior
Besant Technologies
·
Internship
Deployed multiple AWS architectures using EC2, VPC, S3, ALB, and IAM to support simulated workloads and improve availability. Automated infrastructure provisioning with Terraform and CI/CD workflows, reducing deployment time and configuration errors. Used CloudWatch metrics and logs alongside Linux troubleshooting to validate health, investigate failures, and document remediation steps.
Amazon EC2
Amazon S3
IAM
Terraform
Amazon CloudWatch
Linux
Projects
Linux System Administration and Monitoring
Administered Ubuntu user and permission setup and managed services, including Nginx operations. Analyzed system and application logs using systemctl and journalctl for operational troubleshooting. Wrote Bash scripts for routine health checks and documented recovery steps for repeatable support.
Production Ready Microservices Application on AWS
Containerized microservices with Docker, published images to Amazon ECR, and deployed workloads on EC2 via an automated GitHub Actions pipeline. Designed the supporting VPC, IAM, security groups, and Secrets Manager setup for production-like execution. Validated deployments using CloudWatch logs and metrics and investigated runtime issues.
AWS Multi-Tier Architecture
Built a highly available multi-tier AWS architecture using public and private subnets with EC2, RDS, ALB, and Auto Scaling. Configured Route 53 and NAT routing, along with IAM and security groups to control access across tiers. Added CloudWatch monitoring and health checks to observe availability, scaling behavior, and instance performance.
CI/CD Pipeline Deployment on AWS EC2
Created an end-to-end Jenkins and GitHub Actions workflow to build, test, containerize, and deploy an application to AWS EC2. Added repeatable deployment steps plus post-deployment validation to reduce manual errors. Focused on making the pipeline easier to troubleshoot when deployments fail.
Enterprise Multisite Network
Designed a Cisco Packet Tracer multisite topology featuring VLANs, inter-VLAN routing, DHCP, ACLs, NAT, and WAN routing concepts. Validated connectivity and access policies using connectivity tests and packet analysis. Documented addressing, device configuration, and troubleshooting steps for the modeled network.
Technical skills
Languages
5
C++
Python
Java
C
Bash
DevOps
18
AWS
GitHub
Git
Amazon S3
Amazon CloudWatch
Amazon EC2
Linux
Terraform
IAM
TCP/IP
DNS
Kubernetes
Amazon EKS
Grafana
OSPF
VLAN
GitHub Actions
Docker
AI/ML
3
Pandas
Scikit-learn
Google Colab
Other
17
Wireshark
MySQL
Ansible
Nginx
Prometheus
Ubuntu
Jupyter Notebook
CI/CD
Jenkins
Pulumi
DevOps
Debian
DHCP
VPN
SLI/SLO/SLA
Anomaly Detection
Least Privilege
Junior AI/ML Engineer
Confidence: Low Data-centric
Data-centric ML practitioner (junior) focusing on exploratory network traffic analysis and anomaly detection. The strongest proven skill is rapid prototyping of data ingestion and preprocessing with pandas and applying scikit-learn IsolationForest, as shown in Network_Traffic_Anomaly_Detection_Project.ipynb. There is little or no evidence of reproducible experiments, evaluation rigor, production deployment, MLOps or production-grade AWS integration in the analyzed human-authored code.
Model Architecture & Training
2/10
How well models are designed and trained
Uses an off-the-shelf IsolationForest from scikit-learn with no custom architecture, training loop, hyperparameter search or training/evaluation rigor.
Evidence
SAAI-2020/AI-Driven-Network-Anomaly-Detection-and-Traffic-Analysis-On-AWS-Cloud/Network_Traffic_Anomaly_Detection_Project.ipynb: model = IsolationForest(contamination=0.01, random_state=42)
SAAI-2020/AI-Driven-Network-Anomaly-Detection-and-Traffic-Analysis-On-AWS-Cloud/Network_Traffic_Anomaly_Detection_Project.ipynb: df['anomaly'] = model.fit_predict(df_clean[['srcport', 'dstport', 'packets', 'bytes']])
Data Pipeline & Feature Engineering
3/10
How data is prepared for models
Basic data ingestion and preprocessing: reading flow logs, explicit column naming, coercion to numeric types, dropna/fillna and simple feature selection.
Evidence
SAAI-2020/AI-Driven-Network-Anomaly-Detection-and-Traffic-Analysis-On-AWS-Cloud/Network_Traffic_Anomaly_Detection_Project.ipynb: df = pd.read_csv(filename, sep=r'\s+', header=None, names=column_names, engine='python', on_bad_lines='warn', skiprows=2)
SAAI-2020/AI-Driven-Network-Anomaly-Detection-and-Traffic-Analysis-On-AWS-Cloud/Network_Traffic_Anomaly_Detection_Project.ipynb: for col in ['srcport', 'dstport', 'packets', 'bytes']: df[col] = pd.to_numeric(df[col], errors = 'coerce')
Experimentation & Evaluation
1/10
How results are measured and tested
Minimal experimentation and evaluation: ad-hoc plotting and simple aggregate counts printed; no validation splits, metrics, baselines, experiment tracking or reproducibility practices.
Evidence
SAAI-2020/AI-Driven-Network-Anomaly-Detection-and-Traffic-Analysis-On-AWS-Cloud/Network_Traffic_Anomaly_Detection_Project.ipynb: print("Total Logs:", len(df)) and print("Anomaly Detected:", len(anomalies))
SAAI-2020/AI-Driven-Network-Anomaly-Detection-and-Traffic-Analysis-On-AWS-Cloud/Network_Traffic_Anomaly_Detection_Project.ipynb: plt.scatter(df_numeric['packets'], df_numeric['bytes'], label='Normal', alpha=0.5)
MLOps & Deployment
How models are shipped to production
Not evidenced in public code
Computational Efficiency
How efficiently computing resources are used
Not evidenced in public code
Research Depth & Innovation
Depth of research and new ideas
Not evidenced in public code
Verified artifacts
Expertise
Cybersecurity & Threat Intelligence· Junior
Industries
Artificial Intelligence· Junior
Cybersecurity· Junior
Technologies
Python
C++
MySQL
Terraform· since 2022
Ansible
Jupyter Notebook
GitHub Actions· since 2026
Debian
Prometheus
Pulumi· since 2026
Scikit-learn
Google Colab
CI/CD
Jenkins· since 2026
Pandas
Git· since 2020
AWS· since 2022
Docker
Kubernetes
Grafana
Amazon EKS
Amazon EC2· since 2022
SLI/SLO/SLA
Anomaly Detection
GitHub· since 2020
Amazon S3· since 2022
IAM· since 2022
Amazon CloudWatch· since 2022
Linux· since 2022
TCP/IP· since 2023
DNS· since 2023
DHCP
VPN
VLAN· since 2023
OSPF· since 2023
AWS· mentioned only
Cloud· mentioned only
Python· mentioned only
Terraform· mentioned only
Recommendations
- Develop prototype anomaly detection proofs-of-concept that extend the current notebook into a modular pipeline with unit tests and clear input/output schemas.
- Implement proper experiment tracking and evaluation: train/validation splits, meaningful metrics, baseline comparisons and simple hyperparameter sweeps.
- Harden preprocessing and data validation: robust parsing for flow logs, schema checks, and avoid SettingWithCopy operations by using .loc.
- Package and instrument the pipeline for production ingestion (small-scale MLOps): model serialization, reproducible runs, and basic deployment scripts or CI hooks.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Intern Backend Developer
Confidence: Low Generalist
An intern-level practitioner focused on Linux administration and cloud monitoring with an emphasis on hands-on scripts and documentation. The strongest proven skill is documenting Linux system monitoring and troubleshooting procedures as shown in README materials describing Bash scripting and monitoring tasks. There is no human-authored backend source code, service implementations, database migrations, or tests available to evaluate production-grade backend engineering skills.
API Design
How well APIs are designed
Not evidenced in public code
Data Layer & Database
Working with databases
Not evidenced in public code
Scalability & Performance
Handling load and speed
Not evidenced in public code
System Architecture
Overall system structure
Not evidenced in public code
Security & Auth
Protecting data and access
Not evidenced in public code
Reliability & Observability
Stability and monitoring
Not evidenced in public code
Recommendations
- Implement a small end-to-end REST API that exposes monitoring metrics and alerts, with automated tests and a CI pipeline to demonstrate API design, input validation, error handling, and idempotency.
- Convert existing monitoring Bash scripts into human-authored service code (for example, a Python service or AWS Lambda) with explicit timeouts, retries with backoff, and graceful shutdown handling.
- Add a simple database-backed feature with a documented migration history and transaction boundaries, plus unit and integration tests to show data-layer and schema-evolution competence.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Intern DevOps Engineer
Confidence: Low Generalist
Early-stage Linux-focused DevOps practitioner (junior) specializing in hands-on Linux administration and Bash automation. Practical skills are evidenced by documented Linux monitoring and automation tasks such as system_info.sh and cpu_monitor.sh described in the project materials. There is no public human-authored infrastructure, CI/CD, container orchestration, or production-grade observability code to demonstrate operational engineering at scale.
CI/CD Pipelines
Automated build and deploy
Not evidenced in public code
Infrastructure as Code
Managing servers with code
Not evidenced in public code
Containerization & Orchestration
Working with containers
Not evidenced in public code
Observability & Monitoring
Watching system health
Not evidenced in public code
Reliability & Incident Response
Keeping systems up
Not evidenced in public code
Cloud & Cost Optimization
Smart use of the cloud
Not evidenced in public code
Expertise
Observability & Monitoring· Intern
Technologies
DevOps
Ubuntu· since 2026
Nginx
Bash· mentioned only
Recommendations
- Develop and publish a small human-authored repo containing the actual scripts (system_info.sh, cpu_monitor.sh) with executable examples and tests so reviewers can verify technical work.
- Extend Bash automation into a basic Ansible playbook or small Terraform module to demonstrate IaC and environment separation with remote state.
- Create a simple CI pipeline (GitHub Actions) that runs shellcheck, linting, and tests, and shows artifact handling or deployment gating.
- Migrate monitoring checks to Prometheus alerting rules or instrument a small app with OpenTelemetry to show end-to-end observability.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Job preferences
- Work permits
- United States
- Locations
- New York, San Francisco
- Company size
- 201-500
Open to AI agents: they read this profile and start a dialog through Alion
alion.io/@sai-vignesh-kumar/mcpAlion · People, companies and AI agents in one graphalion.io/@sai-vignesh-kumar