Wypoon Technologies

Backend Engineer

• Develop secure, well-tested FastAPI services in Python, with proper async I/O, dependency management, and observability.
• Design and build retrieval-augmented generation (RAG) pipelines using our internal templates.
• Deploy GenAI solutions to Azure Kubernetes Service (AKS) using Docker, Helm, and Azure DevOps pipelines.
• Integrate with Azure OpenAI or open-source models hosted on AKS, including prompt handling, streaming, fallbacks, and cost control.
• Select and tune vector search engines (e.g., Azure AI Search, pgvector on Azure PostgreSQL) and optimize retrieval.
• Collaborate with product and data teams to shape use cases, prioritize features, and iterate from PoC to production.
• Maintain clear documentation and reusable components to support knowledge sharing and adoption.

Generative AI Engineer – Python, Azure, RAG, FastAPI

Network Engineer

• Design and implement network infrastructures using tools like Cisco, Fortinet, Palo Alto, Juniper, Aruba and Arista Networks.
• Configure, manage, and troubleshoot firewalls, routers, switches, and VPNs, and SD-WAN solutions to ensure reliable network connectivity and security.
• Perform network security assessments and implement necessary measures to safeguard network environments.
• Deploy and maintain Aruba ClearPass for network access control, authentication, and policy enforcement.
• Work with cloud-based technologies such as VMware NSX, Cisco ACI, and Juniper Contrail for SDN and network virtualization.
• Automate network tasks using tools like Ansible, Terraform, and other automation frameworks.
• Monitor network performance, identify bottlenecks, and take corrective actions proactively.
• Manage and upgrade network hardware and software to keep up with technological advancements.
• Collaborate with cross-functional teams to ensure smooth integration of network systems with other IT infrastructures.
• Assist in network security measures using advanced solutions like Palo Alto, Check Point, and Zscaler.

Machine Learning Engineer

• Design and implement machine learning pipelines in cloud environments (Azure, AWS, or GCP).
• Develop and deploy models for classification, regression, time series, recommendation, or NLP use cases.
• Work with structured and unstructured data; apply feature engineering, model tuning, and evaluation techniques.
• Package and deploy models using containerization (Docker, Kubernetes).
• Automate and monitor ML workflows using MLflow, Airflow, or cloud-native tools (SageMaker, Vertex AI, Azure ML).
• Collaborate with data scientists, engineers, and product teams to translate business problems into ML solutions.
• Contribute to MLOps practices: model versioning, CI/CD for ML, performance monitoring, and rollback strategies.

Generative AI Engineer – Python, Azure, RAG, FastAPI

Network Engineer

Machine Learning Engineer – Cloud, MLOps, GenAI