Junior Digital Innovation Engineer

The Junior Digital Innovation Engineer supports the R&D team's mandate to explore and prototype emerging AI/ML capabilities, evaluate new technologies, and investigate improved software development approaches. Working under the guidance of the Team Lead and senior team members, the role conducts technology and literature scanning, assists in building proof-of-concept prototypes and feasibility spikes, documents findings clearly, and presents experiment results to the team. The role is expected to grow steadily in independence and technical scope as competence and confidence increase. Technology & Literature Scanning - Researches and tracks developments in AI/ML, emerging frameworks, and developer tooling relevant to the product and team's technology stack. - Summarises findings from articles, papers, vendor releases, and community sources into digestible internal notes. - Flags technologies, platforms, or techniques (e.g., new LLM capabilities, AI/ML frameworks, cloud AI services) that may warrant a deeper spike. - Maintains an up-to-date view of the broader industry landscape relevant to the team's exploration areas. Assisting with Prototypes and Feasibility Spikes - Assists in building small-scale proofs-of-concept and feasibility spikes under the guidance of the Team Lead or senior team members. - Implements clearly-scoped experimental tasks using assigned frameworks, APIs, or platforms (e.g., LangChain, Azure OpenAI, Hugging Face). - Sets up and tears down experimental environments in a controlled, low-risk manner. - Escalates technical blockers or scope uncertainty early rather than stalling on unclear experiments. Documentation of Findings - Documents experiment setup, methodology, results, and conclusions clearly for each spike or proof-of-concept. - Records what worked, what did not, and why, to build the team's shared knowledge base. - Uses agreed tools (e.g., Confluence, Jira) consistently to track spikes and their outcomes. - Keeps documentation current as experiments evolve or are re-scoped. Presenting Experiment Results - Presents proof-of-concept and spike outcomes to the team in a clear, structured manner. - Articulates what was tested, the results observed, and a recommendation on next steps (progress, pivot, or shelve). - Responds to questions from the Team Lead and peers about methodology and findings. - Builds confidence and clarity in presenting technical material as experience grows. Ramp-Up and Learning Velocity - Actively works through onboarding materials, prior spike documentation, and internal knowledge bases. - Pairs frequently with the Team Lead and senior team members to build understanding of the innovation pipeline and evaluation framework. - Builds proficiency with the team's core AI/ML tools, frameworks, and experimentation practices. - Tracks own learning goals and progress with support from a mentor or the Team Lead. Adherence to Experimentation Standards - Follows the team's defined spike/experiment process, tracking conventions, and documentation standards. - Uses agreed tools (e.g., Jira, Confluence, Git) consistently and correctly for tracking spikes. - Applies data-handling, security, and ethical-use guidelines when experimenting with AI/ML tools and third-party platforms. - Applies feedback from Team Lead and senior reviewers consistently in subsequent spikes. Continuous Improvement and Feedback - Attends and engages in team retrospectives and knowledge-sharing sessions, offering observations even as a newer team member. - Acts on constructive feedback from the Team Lead and senior reviewers. - Reflects on own experiment outcomes and demonstrates visible improvement in technical judgement over time. - Builds toward taking on progressively larger and less-supervised exploration work.