• Take full ownership of new connector requests, driving them from initial requirement to production enablement. • Lead and push homologation processes with Security and other internal stakeholders until approval is achieved. • Identify and resolve technical and administrative bottlenecks that slow down adoption. • Coordinate with business and technology teams to ensure use cases progress effectively. • Ensure connectors are not only approved but fully operational and usable in real scenarios. • Provide technical clarity around integrations and connector architecture. • Actively drive adoption of low-code solutions across teams. • Lay the groundwork for future advanced automation initiatives.

• Lead the shift from manual workflows to autonomous operations • Architect agentic workflows for handling multi-step reasoning, tool-calling, and error recovery • Embed with logistics teams to identify high-impact opportunities for agentic solutions • Own the agent lifecycle from discovery to production deployment • Develop robust guardrails, evaluation frameworks, and logging systems • Document and publish reusable agent modules to accelerate AI enablement

• Lead the design, training, and optimization of large-scale language models for African languages and dialects, ensuring accuracy and contextual relevance. • Develop innovative solutions for data pre-processing, tokenization, and embedding, specifically for low-resource and multilingual datasets. • Implement and fine-tune transformer-based architectures (e.g., GPT, BERT) for specific NLP applications like text generation, translation, and sentiment analysis. • Design scalable pipelines for data collection, annotation, and pre-processing to support model development. • Collaborate with linguists, cultural experts, and AI researchers to ensure the LLM aligns with diverse user needs and cultural sensitivities. • Deploy, monitor, and maintain production-level LLM systems in cloud-based environments, ensuring robust performance and reliability. • Establish frameworks for model evaluation, leveraging metrics such as perplexity, BLEU, and human-centered benchmarks. • Stay at the forefront of advancements in NLP, LLMs, and generative AI, integrating emerging research into Awarri's language model development efforts. • Document processes, architectures, and outcomes for knowledge sharing and collaboration across teams.

• Lead the design and development of scalable machine learning infrastructure on AWS. • Work closely with product teams to develop MVPs for AI-driven features. • Create and enhance monitoring and alerting systems for machine learning models. • Enable various departments within the organization to leverage AI/ML models. • Offer expertise in debugging and resolving issues related to machine learning models in production. • Design and scale machine learning architecture to support rapid user growth. • Conduct code reviews, mentor team members, and elevate overall team capabilities through knowledge sharing.