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 environments, utilising infrastructure-as-code and configuration management tool
Ensure seamless system integration and cross-platform compatibility through strategic API design, interoperability testing, and continuous benchmarking.
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, OpenSearch, 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 or AWS CloudFormation.
Familiarity with configuration management tools like Ansible for comprehensive system administration.
Familiarity with AI-powered code assistants (e.g., Amazon Kiro, GitHub Copilot) for accelerating coding processes and enhancing developer productivity.
Good knowledge of European Commission grant management ecosystem (eGrants) would be an asset.
Strong problem-solving and analytical skills.
Strong organisational and time-management skills.
Strong team spirit and experience working effectively within a collaborative team.
Capability of integration in an international/multi-cultural environment.
Excellent communication skills to articulate complex technical concepts.
Attention to detail in infrastructure design and well-structured documentation.
Ability to write clean, efficient, and maintainable code
Summary
Architect and implement secure, interoperable MLOps and data platforms across multi-cloud environments, with CI/CD pipelines, infrastructure-as-code, monitoring, and security scanning. Requires expertise in Docker, Kubernetes, Terraform, relational and non-relational databases, Kafka, RabbitMQ, and strong communication and collaboration skills.
Responsibilities
Architect and implement secure interoperable solutions; develop data platforms for AI and ML workloads; design MLOps pipelines; design security architectures; optimise performance and scalability; collaborate with cross-functional teams; establish CI/CD pipelines; ensure system integration
Qualifications
Expertise in containerisation and orchestration with Docker and Kubernetes; network architecture with hybrid cloud; relational and non-relational databases; monitoring with Prometheus and Grafana; Terraform or AWS CloudFormation; Git, Jenkins, GitLab CI