ML Ops Internship

Propercept AI delivers innovative artificial intelligence solutions designed to help businesses transform and thrive in the digital era. Specializing in chatbot development, computer vision systems, and advanced AI applications, we empower organizations to automate processes, enhance customer experiences, and drive operational efficiency.

Our mission is to bridge the gap between cutting-edge technology and real-world business needs. With expertise in natural language processing, machine learning, and AI-powered automation, Propercept AI provides tailored solutions that enable companies to unlock new levels of performance, productivity, and growth. From smart customer interactions to advanced visual intelligence, we are your partner in AI-driven innovation.

Stipend: Unpaid

Benefits

• Internship Certificate
• Letter of Recommendation

Role and Responsibilities:

As an MLOps Intern at Propercept AI, you will collaborate with our engineering and data science teams to streamline the deployment, monitoring, and maintenance of machine learning models in production. Your responsibilities will focus on operationalizing AI solutions through robust MLOps practices:

• Model Deployment & Pipeline Automation:
  • Assist in building CI/CD pipelines for automated model training, validation, and deployment using tools like GitHub Actions, Jenkins, or Azure DevOps.
  • Containerize ML models and workflows using Docker and orchestrate scalable deployments with Kubernetes.
  • Optimize model inference performance and resource efficiency on cloud platforms (AWS, GCP, Azure).
• Collaboration & Documentation:
  • Work with data scientists to operationalize prototypes into scalable, production-ready solutions.
  • Document MLOps workflows, including pipeline architecture, deployment processes, and monitoring protocols.
  • Support version control for models, datasets, and configurations using Git/DVC.

Candidate Qualifications:

• Pursuing a Bachelor’s/Master’s in Computer Science, Data Engineering, DevOps, or a related field.
• Familiarity with MLOps concepts: CI/CD, containerization, cloud deployment, and model monitoring.
• Proficiency in Python and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Hands-on exposure to cloud platforms (AWS/GCP/Azure) and DevOps tools (Docker, Kubernetes, Git).
• Basic knowledge of infrastructure as code (Terraform, CloudFormation) and workflow orchestration (Airflow, Kubeflow).
• Strong problem-solving skills and passion for automating ML workflows.
• Excellent communication skills to collaborate across technical teams.

Required Skills:

• Python programming
• Docker/Kubernetes
• Cloud platforms (AWS/GCP/Azure)
• CI/CD pipelines
• Monitoring tools (MLflow, Grafana)