Solutions Architect (Data Analytics / Artificial Intelligence)

Responsibilities:

• Analyses, designs and develops digitalisation roadmaps and implementation plans based on a current versus future state solutions architecture
• Analyses the current architecture to identify weaknesses and identifies opportunities for improvement, and performs ongoing architecture quality review activities.
• Leads and facilitates the solutions architecture governance processes by aligning to the enterprise architecture and manages exceptions to architectural standards at a solutions level
• Assesses near-term needs to establish business priorities, analyses and develop solutions architectural requirements, and ensures alignment of architectural requirements with the IT strategy.
• Work with customers to identify, design and develop AI/ML solutions to address their requirements, challenges and pain points.
• Engage solution vendors to determine appropriate products that will meet customer DA/AI requirements
• Support data analytics solutions response to RFQ and tenders, proposal development and provide post-sales support.
• Lead and guide project team to develop and implement the solution
• Exercise and continually develop leadership, management and technical skills

Requirements:

• Bachelor’s degree in Data Science, Artificial Intelligence, Computer Science, Engineering or related disciplines.
• Strong technical knowledge in software architecture, have breadth across technology stacks with deep hands on skills, and passionate about working with customers to help build applications at scale.
• Knowledge of Continuous Integration/Continuous Delivery environment with change/version control process and methodologies.
• Knowledge of networking, computing platform, storage, database, security, middleware, network and systems management, and related infrastructure technologies and practices.
• Experience in designing and building DA/AI solutions (eg cloud native, microservices architecture using containers/Kubernetes), integrated with machine learning pipeline.
• Experience in all stages of the project lifecycle, e.g. planning, requirements gathering, design documentation, testing, rollout and transition to Ops.
• Experience in technical presentation, technical documentation.
• Experience in delivering projects for the defence sector will be a plus.