About the job
Job Title: MLOps Engineer Intern
Company: Aaizel International Technologies Pvt. Ltd.
Location: Gurugram, Haryana (On-site)
Duration: 6 Months (PPO based on performance)
About Aaizeltech
Aaizeltech is a deep-tech company building AI/ML-powered platforms, scalable SaaS applications, cybersecurity solutions, and intelligent embedded systems. We are looking for a passionate MLOps Engineer Intern eager to work on real-world machine learning infrastructure, cloud platforms, and deployment pipelines while collaborating with experienced AI/ML engineers.
Role Overview
As an MLOps Engineer Intern, you will assist in building, deploying, and maintaining machine learning pipelines and cloud infrastructure. You will gain hands-on experience in containerization, Kubernetes, CI/CD automation, model deployment, monitoring, and cloud-native technologies while contributing to production-ready AI solutions.
Key ResponsibilitiesMLOps
- Assist in building and maintaining end-to-end ML pipelines using tools such as Airflow, Kubeflow Pipelines, or Metaflow.
- Collaborate with AI/ML engineers to deploy machine learning models using , TensorFlow and Airflow orchestration.
- Containerize ML applications using Docker with Flask, FastAPI, or Django APIs.
- Support dataset versioning and experiment tracking using MLflow and DVC.
- Assist in maintaining model registries and ensuring reproducible ML workflows.
- Monitor deployed models for performance, data drift, and infrastructure health using tools such as Evidently AI, Prometheus, and Grafana.
- Support automated retraining workflows and model deployment processes.
Cloud & DevOps
- Assist in deploying and managing cloud infrastructure on AWS, GCP, or Azure.
- Support CI/CD pipeline development using GitHub Actions, GitLab CI, or Jenkins.
- Learn and contribute to Infrastructure as Code (IaC) using Terraform or CloudFormation.
- Work with Docker and Kubernetes for application deployment and orchestration.
- Monitor cloud resources and optimize infrastructure performance.
- Follow DevOps and DevSecOps best practices for secure deployments.
Required Skills
- Good understanding of Linux, shell scripting, and networking fundamentals.
- Basic knowledge of Python programming.
- Familiarity with Docker and Kubernetes.
- Understanding of Git and CI/CD concepts.
- Knowledge of cloud platforms such as AWS, Azure, or GCP.
- Basic understanding of machine learning workflows and model deployment.
- Familiarity with REST APIs using Flask, FastAPI, or Django.
- Strong analytical, debugging, and problem-solving skills.
- Good communication and willingness to learn new technologies.
Preferred Skills
- Exposure to MLflow, DVC, Airflow, Kubeflow, or Metaflow, apache kafka.
- Familiarity with Terraform or other Infrastructure as Code tools.
- Knowledge of monitoring tools such as Prometheus or Grafana.
- Understanding of model serving frameworks like BentoML, TorchServe, or TensorFlow Serving.
- Knowledge of DevSecOps tools like SonarQube is an advantage.
Eligibility
- Final-year B.Tech/B.E./M.Tech/MCA students or recent graduates in Computer Science, Information Technology, Artificial Intelligence, Machine Learning, or related fields.
- Strong interest in MLOps, Cloud Computing, DevOps, and AI infrastructure.
- Prior academic projects, internships, or open-source contributions in ML or cloud technologies are a plus.
What You'll Gain
- Hands-on experience with production-grade MLOps and cloud infrastructure.
- Opportunity to work on real-world AI/ML products and scalable deployments.
- Mentorship from experienced AI/ML and DevOps professionals.
- Exposure to modern MLOps tools, Kubernetes, cloud platforms, and CI/CD workflows.
- Potential Pre-Placement Offer (PPO) based on performance.
Who You'll Work With
- AI/ML Engineers, Backend Developers, Frontend Developers, QA Team
-Product Owners, Project Managers, and external Government or Enterprise Clients
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