Lead DevOps, MLOps & Full-Stack Engineer: Building End-to-End Cloud Platforms

Building secure, scalable and reliable systems

Lead Full-Stack, DevOps & MLOps Engineer specializing in frontend and backend application development, automation, security-focused engineering, cloud infrastructure (AWS/Azure), and scalable distributed systems. I design and operate end-to-end platforms—from React frontends and backend microservices to CI/CD pipelines, production deployment, and machine learning operations. I also implement CI/CD pipelines for automated build, signing, and release of Flutter applications for iOS and Android.

Full-Stack: React • NextJS • NodeJS • Python • Java MLOps: Kubeflow • Model CI/CD • Model Serving Mobile: Android Development • Flutter CI/CD • iOS/Android Release Automation CI/CD: GitHub Actions • Azure DevOps • Jenkins Containers: Docker • Kubernetes IaC: Terraform • AWS CloudFormation • Azure ARM Templates • Ansible DevSecOps: SAST • DAST • OWASP

Tip: press T to toggle theme.

Core Expertise

Frontend Development

Build responsive user interfaces using NextJS, React and TypeScript frameworks

Backend & APIs

Develop RESTful and event-driven microservices using NodeJS, NextJS, Python (Django/FastAPI/Flask), and Java (Spring Boot)

System Integration

Design and integrate APIs and messaging systems (Kafka/RabbitMQ) for scalable, event-driven distributed architectures

Application Deployment

Containerize and deploy full-stack applications using Docker and Kubernetes

Cloud & IaC

Cloud infrastructure provisioning using Infrastructure as Code, secure networking, fault-tolerant design

CI/CD & Release Engineering

Test-driven deployment, versioning, artifact traceability

Mobile Delivery

Automated Flutter build, signing, versioning, and release pipelines for Android and iOS applications.

Observability & Monitoring

Metrics, logging, alerting, and incident detection using tools like Prometheus and Grafana

ML Workflows

Kubeflow setup and management for end-to-end ML pipelines.

Model Serving

Model serving solutions using KServe, Seldon and FastAPI.

CI/CD

Automation of ML training and deployment workflows.

Monitoring

ML system monitoring and performance tracking.

Android

Android application development (Java/Kotlin) with API integration.

CI/CD

CI/CD automation for Android and iOS builds, code signing, versioning, and release.

Security

Comprehensive mobile security assessment including code analysis, runtime testing, API security, authentication, and data protection.

Performance

Mobile application performance optimization, debugging, and crash analysis.

Core Skills

DevOps

CI/CD: automated testing, validation and gated deployments using GitHub Actions, Azure DevOps, Jenkins
Containers: Docker, Kubernetes orchestration, event-driven autoscaling using KEDA
Cloud & On-Premise: On-Premise, AWS & Azure infrastructure provisioning, networking, and system administration
IaC: Terraform, CloudFormation, ARM Templates, Ansible
Messaging & Streaming: Apache Kafka, RabbitMQ and Redis for event-driven and distributed systems
DevSecOps: OWASP, SAST, DAST, SonarQube, Dependency Check, Secure Pipelines Gates
Observability: monitoring, logging, alerting using Prometheus and Grafana

MLOps

Kubeflow: setup and management of ML pipelines (training, testing, deployment, scaling)
Model Serving: KServe, Seldon, FastAPI and Scala microservices
ML Workflows: Event-driven ML pipelines for scalable ML workflows
Data Engineering: large-scale data processing using Apache Spark, data versioning with LakeFS
ML CI/CD: automated training, validation, and deployment workflows
ML Monitoring: tracking model performance, reliability, and inference metrics
Support: troubleshooting and optimization of ML systems

Full-Stack

Frontend: React, Vue, Angular, TypeScript
Backend: NodeJS, NextJS, Python (FastAPI/Flask/Django), Java (Spring Boot)
API Design: RESTful services and microservices architecture
Auth: Keycloak, OAuth2, OIDC, JWT
Mobile: Android (Java/Kotlin), J2ME, Windows Mobile (C#)
Databases: Relational (MySQL, PostgreSQL, SQLite), NoSQL (MongoDB, Couchbase, CouchDB), Caching (Redis), Search (Elasticsearch), Graph (Neo4j)
Integration: APIs, API gateways, and distributed systems
Healthcare Interoperability: HL7 FHIR

Experience

Lead DevOps & MLOps Engineer Remote • 3+ years total experience

Designed system architectures and delivery pipelines with security best practices.
Developed backend microservices (NodeJS, Python) supporting ML pipelines and distributed workloads.
Implemented Keycloak-based and JWT authentication to secure APIs and microservices.
Led DevOps team in provisioning AWS and Azure infrastructure using IaC scripts.
Led DevOps team in implementing CI/CD pipelines for automated code analysis, testing, OWASP security scans, and deployments.
Implemented CI/CD pipelines for Flutter app build, signing, versioning, and release (Android & iOS).
Maintained Kubeflow environment and automated ML training, testing and deployment.
Designed and implemented event-driven ML pipelines for scalable ML workflows.
Implemented scalable model serving using FastAPI and Kserve.
Worked with healthcare data standards like HL7 FHIR and DICOM to support secure data exchange and imaging workflows.
Led DevOps team in containerizing frontend, backend, and ML services using Docker for scalable deployment on Kubernetes clusters.
Implemented autoscaling of containerized services on Kubernetes using KEDA for event-driven workloads.
Led implementation of Kubernetes logging, monitoring, and alerting solutions for Kubernetes clusters.
Led diagnosis and resolution of deployment failures in Kubernetes environments to ensure system reliability.

Full Stack & MLOps Engineer Remote • 3+ years total experience

Built responsive frontend applications using React.
Designed and implemented scalable microservices using Node.js, NestJS, and Flask.
Set up and maintained Kubeflow environments for ML workflows.
Automated training, testing, and deployment of machine learning models.
Developed ML microservices using FastAPI, Spring Boot and Scala.
Designed and implemented scalable, event-driven ML pipelines.
Implemented CI/CD pipelines for automated code, analysis, tests and deployment on Kubernetes clusters.
Containerized frontend applications, backend, and ML services using Docker for scalable deployment on Kubernetes clusters.
Implemented autoscaling of containerized services on Kubernetes using KEDA.
Implemented logging, monitoring, and alerting for Kubernetes clusters
Troubleshot and resolved ML deployment failures in Kubernetes environments to ensure reliability.

Beyond Software Engineering

Diagnostics, motorsport media and management.

Motorcycle Diagnostics

Diagnose motorcycle performance and mechanical issues, including engine behavior, fueling, sensors, and electronic systems, to improve reliability and performance.

Racer Management

Build racer’s brand, secure opportunities, and align performance with visibility and sponsorship value.

Motorsport Photography

Capture high-performance motorsport events including WRC rallies, rallycross, time attack, drag and track racing, with a focus on motion, atmosphere, and storytelling.

Social Management

Manage content strategy, audience growth, and engagement for motorsport-focused platforms, optimizing reach and consistency.