AI Engineer - Deep Learning Research (Manager)

At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal.

The AI Transformation and Research role is a strategically focused AI engineer responsible for identifying emerging AI opportunities, evaluating new models and technologies, and shaping the organization’s AI direction through evidence-based research, experimentation, and portfolio recommendations. The role bridges the gap between enterprise AI strategy and technical reality by assessing what is worth adopting, what should be monitored, what should be tested, and what should not be pursued. It supports decision-making across the AI ecosystem by producing objective comparisons of closed-source and open-source models, vendor offerings, deployment patterns, and cost-performance tradeoffs. This role is not primarily responsible for building production solutions or delivering business-unit implementations. Instead, it provides the research, validation, and strategic recommendations that help the broader AI team decide where to invest, what to scale, and how to design future AI capabilities. The role requires strong analytical judgment, structured research capability, hands-on understanding of LLMs and AI infrastructure, and the ability to translate technical findings into clear business and executive recommendations. It also requires close collaboration with AI Engineering, AI FDE, Architecture, Operations, and governance teams to ensure alignment with enterprise standards and operational realities.

Monitor the AI landscape for new models, platforms, vendors, tools, techniques, and research developments. Maintain a model landscape / model platter covering leading closed-source, open-source, and locally hosted model options. Evaluate vendor claims and conduct structured benchmarks and comparative evaluations of AI models and platforms. Assess fit-for-purpose use of models for selected enterprise workloads, including when smaller, open, or locally hosted models may be better. Design and run controlled experiments, translate findings into recommendations, and support governance, handoff, and executive reporting. Design and develop prototypes and experimental AI solutions that can be transitioned to production. Provide regular technical updates and findings to FDE for solution shaping.

Master’s degree in AI, Computer Science, Artificial Intelligence, Engineering, or a related field. At least 3 years of experience in AI, data science, software engineering, technology research, or technical consulting. Demonstrable experience evaluating AI solutions, models, tools, or platforms. Strong ability to translate technical findings into clear recommendations. Experience working with cross-functional stakeholders. Familiarity with cloud AI platforms, LLMs, and enterprise technology environments. Strong documentation and presentation skills. Experience with experimental thinking, benchmarking, or structured analysis is preferred. Hands-on technical capability is a plus, especially for model testing and experimentation.

Soft skills:  Strong analytical thinking and structured problem solving Clear executive communication and concise writing Ability to explain complex AI topics in business language Stakeholder management and cross-functional collaboration Curiosity, judgment, and independent thinking Ability to work in ambiguous and fast-changing environments Hard skills:  LLMs, model evaluation, and benchmarking Understanding of open-source and closed-source model ecosystems Python and basic experimentation workflow AI infrastructure literacy, including cloud deployment concepts Prompting, RAG, fine-tuning, and model comparison concepts Data analysis and evaluation methods Familiarity with GCP and/or other major cloud AI platforms Cost-performance analysis and technology