Company Description Wibix AI is a cloud security platform designed to help organizations safeguard, monitor, and stay compliant with evolving cloud threats. Tailored for startups to large enterprises, it offers complete visibility across multi-cloud environments, comprehensive posture management, and identifies misconfigurations while ensuring adherence to industry standards and best practices. With end-to-end automation—from detection to remediation—alongside robust incident management and dynamic reporting, Wibix AI effortlessly strengthens and secures cloud environments. Role Description This is a remote role for a Digital Marketing Intern. The Digital Marketing Intern will be responsible for assisting with social media marketing, digital marketing campaigns, and web analytics. Key tasks include creating and scheduling social media posts, analyzing online marketing trends, and supporting the development and implementation of online marketing strategies. The intern will also work on improving communication strategies and engaging with online audiences. Qualifications Social Media Marketing and Online Marketing skills Linkedin promotions, SEO , CTR, Content management. Digital Marketing and Web Analytics skills Excellent written and verbal communication skills Ability to work collaboratively environment Knowledge in cloud security and technology is a plus Currently pursuing or recently completed a degree in Marketing, Business, Communications, or a related field

Job Description - AI Data Scientist Location: Remote Department: Data & AI Engineering Employment Type: Full-time Experience Level: Mid-level About the Role: We are seeking an experienced AI Data Engineer to design, build, and deploy data pipelines and ML infrastructure to power scalable AI/ML solutions. This role involves working at the intersection of data engineering, MLOps, and model deployment—supporting the end-to-end lifecycle from data ingestion to model production. Key Responsibilities: Data Engineering & Development Design, develop, and train AI models to solve complex business problems and enable intelligent automation. Design, develop, and maintain scalable data pipelines and workflows for AI/ML applications. Ingest, clean, and transform large volumes of structured and unstructured data from diverse sources (APIs, streaming, databases, flat files). Build and manage data lakes, data warehouses, and feature stores. Prepare training datasets and implement data preprocessing logic. Perform data quality checks, validation, lineage tracking, and schema versioning. Model Deployment & MLOps Package and deploy AI/ML models to production using CI/CD workflows. Implement model inference pipelines (batch or real-time) using containerized environments (Docker, Kubernetes). Use MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI) for model tracking, versioning, and deployment. Monitor deployed models for performance, drift, and reliability. Integrate deployed models into applications and APIs (e.g., REST endpoints). Platform & Cloud Engineering Manage cloud-based infrastructure (AWS, GCP, or Azure) for data storage, compute, and ML services. Automate infrastructure provisioning using tools like Terraform or CloudFormation. Optimize pipeline performance and resource utilization for cost-effectiveness. Requirements: Must-Have Skills Bachelor's/Master’s in Computer Science, Engineering, or related field. 2+ years of experience in data engineering, ML engineering, or backend infrastructure. Proficient in Python, SQL, and data processing frameworks (e.g., Spark, Pandas). Experience with cloud platforms (AWS/GCP/Azure) and services like S3, BigQuery, Lambda, or Databricks. Hands-on experience with CI/CD, Docker, and container orchestration (Kubernetes, ECS, EKS). Preferred Skills Experience deploying ML models using frameworks like TensorFlow, PyTorch, or Scikit-learn. Familiarity with API development (Flask/FastAPI) for serving models. Experience with Airflow, Prefect, or Dagster for orchestrating pipelines. Understanding of DevOps and MLOps best practices. Soft Skills: Strong communication and collaboration with cross-functional teams. Proactive problem-solving attitude and ownership mindset. Ability to document and communicate technical concepts clearly.