2026 Internship Cloud Infrastructure Lab Technician

Samsung SDS is a global IT services and solutions leader that helps enterprises accelerate digital transformation. Combining deep industry expertise with advanced technologies — including cloud, AI, data analytics, and secure enterprise platforms — Samsung SDS America, the North America subsidiary, designs and delivers scalable, outcome-driven solutions that improve operational efficiency, reduce risk, and unlock new business value across finance, manufacturing, retail, and public sectors.

At Samsung SDS America, we take the most advanced concepts in Cloud and AI and turn them into scalable, real-world enterprise solutions. As a Managed Service Provider (MSP) leader, we don't just recommend technology—we architect, deploy, and optimize it.

We are looking for a detail-oriented and technically driven Cloud Infrastructure Lab Technician Intern to join our Managed Service Provider (MSP) Operations team. This role is designed for candidates interested in the mechanical and architectural side of cloud computing—focusing on how complex IT environments are built, managed, and optimized for global enterprises.

You will gain hands-on experience in high-level lab management, assisting in the deployment of cloud-native infrastructure, security frameworks, and cost-optimization (FinOps) models. This position provides a unique vantage point into the lifecycle of enterprise IT services, from initial prototype to validated customer-ready solutions.

Eligibility Requirements

• Currently enrolled in an accredited Undergrad or Graduate program (Graduation date: 2026 or 2027).
• Must be authorized to work in the U.S. without current or future sponsorship.
• Able to commit to a 10-week, 40-hour/week program starting June 8th, 2026.
• Ability to commute to our Ridgefield Park, NJ office at least 3 days per week.

Position Summary

Curious about how prototypes evolve into customer-ready offerings and how MSP organizations operate? You’ll gain hands-on experience building real-world lab environments, learning how cloud, security, and FinOps solutions are architected, deployed and validated. You’ll also gain exposure to automation, infrastructure‑as‑code, monitoring, and cost‑optimization practices all while collaborating with experienced professionals across strategy, tech, and operations.

As an intern, you’ll work alongside cross-functional teams to build lab environments for data and AI accelerators and help craft go-to-market strategies for designing and delivering our MSP offerings. You will also contribute to the team’s KPIs by strengthen our capability to respond quickly to customer needs and enhance the quality and repeatability of our solution demonstrations.

Responsibilities

• Collaborate with the Strategy and Operations teams to build "sandbox" environments for testing new service offerings.
• Assist in the execution of FinOps & DataOps practices by identifying underutilized resources and recommending cost-saving configurations.
• Support the development of standard operating procedures (SOPs) for lab governance and security compliance.
• Provision, configure, and manage internal lab environments across major cloud platforms (AWS, Azure, or GCP).
• Maintain comprehensive technical documentation for lab topologies, including network diagrams and configuration logs.

Requirements

• Foundational understanding of cloud architecture, services and platforms (Azure, AWS, or GCP)

• Basic knowledge of Data management and AI
• Familiarity with scripting or automation tools (PowerShell, Python, Bash, or Terraform)
• Interest in data engineering, analytics and automation principles
• Ability to follow technical documentation and standard operating procedures
• Strong problem‑solving skills and willingness to learn new technologies
• Good communication skills and ability to collaborate with technical teams

Preferred Qualifications

• Familiarity with cloud data services (Azure Data Factory, Synapse, AWS Glue, BigQuery, etc.)
• Basic understanding of data engineering concepts (ETL/ELT, data pipelines, data lakes)
• Exposure to SQL and at least one programming language (Python preferred)
• Understanding of machine learning fundamentals (training, inference, model lifecycle)
• Experience with Jupyter notebooks or similar environments
• Basic knowledge of containerization (Docker) and orchestration concepts
• Awareness of MLOps concepts (model versioning, deployment, monitoring)
• Azure Machine Learning, AWS SageMaker, or Google Vertex AI
• Databricks or similar Spark-based platforms
• Git/GitHub for version control
• Terraform or IaC basics for provisioning data ser