1.Designing technology systems:Plan and design the structure of technology solutions, and work with design and development teams to assist with the process.
2.Communicating:Communicate system requirements to software development teams, and explain plans to developers and designers. They also communicate the
value of a solution to stakeholders and clients.
3.Managing Stakeholders:Work with clients and stakeholders to understand their vision for the systems. Should also manage stakeholder expectations.
4.Architectural Oversight:Develop and implement robust architectures for AI/ML and data science solutions, ensuring scalability, security, and performance. Oversee
architecture for data-driven web applications and data science projects, providing guidance on best practices in data processing, model deployment, and end-to-end
workflows.
5.Problem Solving:Identify and troubleshoot technical problems in existing or new systems. Assist with solving technical problems when they arise.
6.Ensuring Quality:Ensure if systems meet security and quality standards. Monitor systems to ensure they meet both user needs and business goals.
7.Project management:Break down project requirements into manageable pieces of work, and organise the workloads of technical teams.
8.Tool & Framework Expertise:Utilise relevant tools and technologies, including but not limited to LLMs, TensorFlow, PyTorch, Apache Spark, cloud platforms (AWS,
Azure, GCP), Web App development frameworks and DevOps practices.
9.Continuous Improvement:Stay current on emerging technologies and methods in AI, ML, data science, and web applications, bringing insights back to the team to foster continuous improvement.