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Field
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. - Experience using ETL processes, APIs, or custom scripts for academic applications. · Experience with machine learning algorithms and predictive modeling. · Familiarity with statistical methods (e.g
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and business leaders Technical Expertise Cloud-native architectures preferably Google Cloud (AWS, Azure, GCP) and SaaS ecosystems Experience with microservices, API-led development, and event-driven
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applicant will also bring significant experience with general software engineering, with working experience with Cloud Platforms (Google Cloud Platform, AWS, Azure), alongside a strong understanding of recent
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every box. Familiarity with LLM development tools such as the OpenAI Assistants API, LangChain (for agent workflows and RAG pipelines), Gemini Studio on Google Vertex AI (for long-context prompt
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experience with building web applications, form builders, and automation tools (e.g. AEM, Make.com, Google Developer Console, Typeform, Inxmail, Mailchimp, Zapier, Wix Velo) Proficiency in JavaScript and
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LLM APIs (e.g. openai-python or google-genai) would be a definite plus. Assist in literature reviews and summarising academic research. Contribute to writing research papers and policy reports
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out: Designing conversational flows for smart agents based on large language models (LLMs). Integrating external tools using APIs. Refining interaction prompts with language models, adjusting
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collaboration platforms (e.g., Microsoft 365, Box, Google Workspace, Zoom, Slack). Familiarity with AI services such as AWS Bedrock, Azure OpenAI, or similar platforms. Proficiency in scripting and automation
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to the external community. Provide reports on website performance using Google Analytics and web analytics. Evaluate existing websites, create plans for improvements, and implement changes in collaboration with
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), Information Retrieval (IR), Retrieval-Augmented Generation (RAG), and AI/ML-based systems). Build and manage data pipelines using open-source or cloud tools (e.g., Apache Airflow, AWS Managed Workflows, Google