Gramian Consulting Group

6 Total Employees

Jobs at Gramian Consulting Group

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Remote
United States
Artificial Intelligence • Information Technology • Professional Services • Consulting
Evaluates and compares chatbot responses against business requirements and quality criteria. Gathers requirements, documents functional specifications, analyzes workflows and data, creates reports and dashboards, supports testing and UAT, tracks project risks and KPIs, and presents evidence-based findings. Collaborates with technical teams and stakeholders to improve AI output quality while maintaining evaluation consistency and meeting deadlines.
Artificial Intelligence • Information Technology • Professional Services • Consulting
Designs and refines executive PowerPoint presentations by transforming complex business information, research, and data into clear visual narratives. Responsibilities include slide reconstruction, data visualization, content editing, narrative evaluation, formatting, stakeholder collaboration, and delivering polished client-ready materials. The role also provides presentation examples and insights to support AI training and evaluation while meeting deadlines remotely.
Artificial Intelligence • Information Technology • Professional Services • Consulting
Design, implement, test, debug, and evaluate production-grade Rust backend systems for AI training projects. Responsibilities include API and persistence development, asynchronous and concurrent processing, performance optimization, reliability analysis, architecture review, reproducible test environments, containerized workflows, and technical documentation. The role requires identifying correctness, scalability, and failure-handling issues while delivering high-quality implementation artifacts and meeting weekly project deadlines.
Artificial Intelligence • Information Technology • Professional Services • Consulting
Develop and validate machine learning models, training pipelines, inference systems, and numerical methods using Python. Debug algorithmic and system-level issues, optimize memory usage and performance, design systematic tests, and build reproducible workflows. Review challenging ML engineering tasks, use coding agents in development, document technical findings, and deliver high-quality work independently within deadlines.