• Design and implement cloud infrastructure primarily on AWS, including networking, security, runtime, data, and logging layers • Support and integrate with Azure and GCP environments where part of broader enterprise architectures • Build and maintain CI/CD pipelines for: • Application deployments (frontend and backend) • Serverless and container-based services • Infrastructure-as-Code promotion across environments using Terraform • Define environment strategy (dev / stage / prod), release workflows, and deployment safety mechanisms (rollback, blue/green) • Implement security and compliance guardrails (encryption, secrets management, IAM, audit logging, WAF/edge security) • Own observability and SLO readiness (monitoring, alerting, tracing, log aggregation, synthetic checks) • Support connectivity and operational patterns across enterprise systems, APIs, and third-party integrations • Partner with architects and engineers on capacity planning, performance optimization, cost control, and incident response

• Design, build, and maintain scalable and secure cloud infrastructure for AI and Big Data solutions, ensuring high availability and performance in a production environment. • Implement end-to-end automation for the complete Machine Learning (ML) lifecycle, including infrastructure for model training, real-time inference, and data processing. • Develop, manage, and optimise Infrastructure as Code (IaC) using tools like Terraform to provision and configure cloud resources consistently and efficiently. • Create, manage, and improve robust Continuous Integration and Continuous Delivery (CI/CD) pipelines to enable rapid and safe deployment of code, models, and infrastructure changes. • Manage and optimise Kubernetes clusters (e.g., AKS, EKS, or GKE) specifically tailored for running containerised data processing frameworks and GPU-enabled ML workloads at scale. • Establish advanced monitoring, logging, and alerting systems to ensure the health, performance, and cost efficiency of the data and AI platforms. • Implement security measures, access controls, and compliance frameworks to ensure data privacy, model governance, and adherence to security best practices across all environments. • Work closely with Data Scientists, AI Engineers, and Software Development teams to translate complex AI requirements into robust, deployable infrastructure solutions.

• Design and implement highly available, scalable, and secure cloud infrastructure architectures utilising Microsoft Azure services, including AKS, VNETs, Private Endpoints, Azure Firewall, Key Vault, and Azure Application Gateway. • Develop and maintain Infrastructure as Code using tools like Terraform or ARM Templates to provision and manage cloud resources in a repeatable and consistent manner. • Design, implement, and optimise robust CI/CD pipelines (e.g., using Azure DevOps, GitHub Actions, or GitLab CI) for both application deployment and infrastructure changes. • Administer, secure, and optimise Kubernetes clusters (preferably AKS), focusing on networking, scaling, security hardening, and performance tuning. • Implement comprehensive monitoring, logging, and alerting solutions using tools like Prometheus, Grafana, and Azure Monitor/Log Analytics to maintain platform health and identify issues proactively. • Enforce security best practices, implement policies (e.g., Azure Policies), and ensure all deployments comply with industry standards and internal IT security requirements. • Work closely with development, security, and product teams to define requirements, troubleshoot complex production issues, and mentor junior team members on DevOps methodologies and tools.

• Capture customer requirements technically, structure them, and translate them into clear implementation plans • Build and roll out fonio setups into production (workflows, logic, data flows, prompts, guardrails) • Implement integrations with third-party systems (APIs, webhooks, CRM, calendar, ticketing, payments, etc.) • Develop small to medium features/glue code to accurately implement customer use cases (including tests/monitoring/logging) • Real-time debugging: calls, flows, data, edge cases — and quick fixes under production conditions • Standardization: reusable templates, best practices, “deployment playbooks” to make setups scalable • Feedback loop into the product: pain points, feature gaps, recurring patterns — prioritized and concrete