Freelancers using Smartcat in Buenos Aires

Freelancers using Smartcat in Buenos AiresFreelancers using Smartcat in Buenos Aires

Projects People

Buenos Aires, Argentina

AI Operations & Global Expansion Strategist

AI Operations & Global Expansion Strategist

Cover image for I engineered a phase-gated framework

I engineered a phase-gated framework in Smartcat to execute the localization of policy-sensitive guidelines for a UNESCO-aligned initiative, managing compliance risks across asynchronous global teams. Key Architectural Results: • Velocity Optimization: Improved approval turnaround by 25% via structured SLAs and automated glossary enforcement. • Risk Mitigation: Reduced late-stage revision cycles by 30% using a rigorous LQA taxonomy framework. • Lifecycle Efficiency: Achieved 100% on-time delivery while maintaining strict UI/UX and data integrity.

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Cover image for I engineered a continuous localization

I engineered a continuous localization and quality governance framework for an international web platform, transforming fragmented workflows into an enterprise-grade operation. I deployed a 4-stage automated pipeline via Smartcat to ensure 100% data traceability and zero UI/UX breakage. Key Architectural Results: • Process Integrity: Eliminated operational bottlenecks by institutionalizing strict entry/exit gate criteria. • Execution Velocity: Maintained zero missed milestones by establishing proactive risk-aware sprint protocols. • Scaled a repeatable, cross-border deployment pipeline.

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Cover image for I engineered multilingual QA governance

I engineered multilingual QA governance and RAG evaluation systems for frontier models at Turing, optimizing personalization across 5,000+ data samples. I built a scalable training system by designing structured error taxonomies and automated debug metadata workflows. Key Architectural Results: • Model Optimization: Cut model output inconsistencies by 18% through multi-turn SxS evaluation. • Sprint Alignment: Synchronized cross-functional teams with rapid Agile RLHF deployment cycles. • Risk Management: Mitigated hallucinations by tracking semantic drift and grounding metrics.

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Cover image for I led model alignment and

I led model alignment and evaluation for Russian voice synthesis models at micro1, closing gaps in phonetic grounding and prosodic naturalness by converting human evaluations into engineering-ready data pipelines. Key Architectural Results: • Audited 1,000+ multimodal data samples, cutting output inconsistencies by 18%. • Engineered a structured error taxonomy for phonetic features and pitch contours. • Integrated HITL validation into sprint cycles, reducing model training latency. • Standardized rubrics to optimize production-facing alignment.

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