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About the role
Must-Have Core Skills & Expectations Python (5+ years): Strong backend development, scalable coding, API development experience. Cloud (AWS + Azure): Hands-on deployment, serverless (Lambda/Azure Functions), cloud services usage. Infrastructure as Code: Terraform for automated provisioning and environment setup. DevOps & CI/CD: Experience with pipelines (Azure DevOps), Git, automated testing & deployments. API & Integration: Strong REST API design and third-party integrations. AI/ML & LLMs: Practical experience with LLMs, RAG, prompt engineering in real applications. Cloud Automation: Building orchestration, provisioning, and optimisation systems. Containers: Docker and Kubernetes for production workloads. Architecture: Scalable system design, design patterns, decision-making capability. Leadership: Mentoring, code reviews, ownership of modules/features. Security: Cloud security fundamentals (IAM, encryption). Good-to-Have Skills & Expectations Advanced AI/ML: Experience with TensorFlow/PyTorch, predictive scaling, anomaly detection. Monitoring: Tools like Prometheus, Datadog for observability. Python Frameworks: Django or Flask for structured service development. Serverless & Event-driven design: Advanced use of cloud-native architectures. SDK/Platform Experience: Building reusable tools/platforms for developers. Data Skills: Data analysis and large dataset handling. Agile: Experience working in Agile/Scrum environments. Soft Skills: Solid communication, collaboration, documentation abilities. Must-Have Core Skills & Expectations Python (5+ years): Strong backend development, scalable coding, API development experience. Cloud (AWS + Azure): Hands-on deployment, serverless (Lambda/Azure Functions), cloud services usage. Infrastructure as Code: Terraform for automated provisioning and environment setup. DevOps & CI/CD: Experience with pipelines (Azure DevOps), Git, automated testing & deployments. API & Integration: Strong REST API design and third-party integrations. AI/ML & LLMs: Practical experience with LLMs, RAG, prompt engineering in real applications. Cloud Automation: Building orchestration, provisioning, and optimisation systems. Containers: Docker and Kubernetes for production workloads. Architecture: Scalable system design, design patterns, decision-making capability. Leadership: Mentoring, code reviews, ownership of modules/features. Security: Cloud security fundamentals (IAM, encryption). Good-to-Have Skills & Expectations Advanced AI/ML: Experience with TensorFlow/PyTorch, predictive scaling, anomaly detection. Monitoring: Tools like Prometheus, Datadog for observability. Python Frameworks: Django or Flask for structured service development. Serverless & Event-driven design: Advanced use of cloud-native architectures. SDK/Platform Experience: Building reusable tools/platforms for developers. Data Skills: Data analysis and large dataset handling. Agile: Experience working in Agile/Scrum environments. Soft Skills: Solid communication, collaboration, documentation abilities.
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