TASK TO BE PERFORMED
• Architect and implement secure, interoperable solutions using standard frameworks and design
patterns, integrating application and infrastructure to achieve scalability and efficiency.
• Develop robust, vendor-agnostic data platforms for AI and machine learning workloads,
optimising multi-cloud environments for performance and flexibility. Design of structured
MLOps pipelines automating and streamlining the models’ lifecycle, from development to
serving.
• Design security architectures that protect infrastructure and applications, aligning with risk
management strategies and regulatory data protection and security standards.
• Continuously assess and optimise system performance and scalability using advanced metrics
and predictive analytics.
• Collaborate with cross-functional teams, including AI developers, business analysts, and IT
operations, to ensure architecture aligns with business objectives and technological capabilities.
• Establish CI/CD pipelines to support rapid application development and deployment in dynamic• Establish CI/CD pipelines to support rapid application development and deployment in dynamicenvironments, utilising infrastructure-as-code and configuration management tools.
• Ensure seamless system integration and cross-platform compatibility through strategic API
design, interoperability testing, and continuous benchmarking.
KNOWLEDGE AND SKILLS
• Expertise in containerisation and orchestration using Docker and Kubernetes for scalable
application deployment.
• Expertise in network architecture design, particularly integrating distributed systems with hybrid
cloud solutions.
• Comprehensive experience with relational and non-relational databases, optimising them for high-
volume transaction processing (e.g., Oracle, PostgreSQL, MongoDB).
• Proficiency with advanced monitoring tools like Prometheus and Grafana to enhance infrastructure
monitoring and real-time alerting.
• Experience with advanced load balancing solutions and caching technologies (e.g. Redis) to support
high-traffic applications.
• Knowledge of security and vulnerability scanning tools (e.g., OWASP, SonarQube), implementing
secure coding practices including robust encryption and identity management.
• Profound knowledge of version control systems like Git and integration platforms such as Jenkins,
GitLab CI.
• Proficiency in distributed streaming and message queue technologies such as Kafka and RabbitMQ
to efficiently manage data flows and support scalable data processing architectures.
• Proficiency in using infrastructure-as-code like Terraform and AWS CloudFormation.
• Familiarity with configuration management tools like Ansible for comprehensive system
administration.
• Familiarity with AI-powered code assistants (e.g., Amazon Q, GitHub Copilot) for accelerating
coding processes and enhancing developer productivity
LEVEL OF EDUCATION
Bachelor Degree