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Vetted Containerization Professionals
Pre-screened and vetted.
Senior DevSecOps Engineer specializing in cloud automation, CI/CD, and compliance
Mid-level Software Engineer specializing in cloud-native distributed systems and AI (RAG)
Senior AWS DevOps Engineer specializing in cloud infrastructure and CI/CD automation
Senior Software Engineer specializing in backend, AWS cloud infrastructure, and data pipelines
Junior Software Engineer specializing in cloud-native backend and LLM applications
Senior Software Developer specializing in LLM prompt engineering and RAG systems
Junior AI/ML Engineer specializing in Generative AI production systems
Junior Full-Stack Software Engineer specializing in cloud-native web apps and APIs
Mid-level Full-Stack Software Engineer specializing in logistics microservices
Mid-level Software Engineer specializing in high-performance systems and hardware validation
Mid-Level Full-Stack .NET Engineer specializing in cloud, APIs, and data analytics
Mid-Level Software Engineer specializing in cloud microservices and AI automation
Mid-level DevOps/Cloud Engineer specializing in CI/CD, IaC, and Kubernetes on AWS/Azure
Senior Full-Stack/Backend Engineer specializing in distributed systems and cloud-native platforms
Anupriya Palanisamy
Mid-Level Software Engineer specializing in Java microservices and event-driven systems
“Backend-focused engineer with experience spanning research and healthcare: owned a Python/SQL data pipeline that transformed vulnerability-fix code data from SQLite into model-ready JSON for LLM analysis. Also deployed Dockerized Spring Boot microservices to Kubernetes with Jenkins CI/CD and built Kafka-based real-time event streaming (appointment/report events) with idempotent consumers to avoid duplicate processing.”
Balakrishna Mylapilli
Mid-level AIML Engineer specializing in production ML and MLOps
“ML practitioner who built a production customer risk scoring system to replace slow manual approvals, owning the full pipeline from feature engineering and XGBoost training to deploying a Dockerized FastAPI prediction service. Emphasizes reliability and business-aligned evaluation (recall/ROC-AUC, threshold tuning, drift monitoring) and is comfortable translating model decisions into stakeholder metrics like conversion rate (experience at EasyBee AI).”