• Design, implement, and maintain evaluation frameworks to measure model accuracy, robustness, latency, and real-world performance across ASR and NLP systems. • Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration. • Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements. • Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation. • Partner with applied research to translate experimental improvements into production-ready systems. • Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals. • Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale. • Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

• Design, implement, and maintain evaluation frameworks to measure model accuracy, robustness, latency, and real-world performance across ASR and NLP systems. • Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration. • Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements. • Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation. • Partner with applied research to translate experimental improvements into production-ready systems. • Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals. • Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale. • Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

• Design, implement, and maintain evaluation frameworks to measure model accuracy, robustness, latency, and real-world performance across ASR and NLP systems. • Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration. • Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements. • Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation. • Partner with applied research to translate experimental improvements into production-ready systems. • Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals. • Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale. • Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

• Define and lead the technical vision for Cresta’s next-generation Agentic AI systems, including Agentic Assist and enterprise AI Agents. • Architect scalable, production-grade LLM systems that integrate reasoning, retrieval, planning, tool use, and real-time decision-making into cohesive, intelligent workflows. • Design and evolve multi-agent orchestration frameworks that combine RAG, structured knowledge, domain-adapted models, and automated actions. • Establish best practices for building robust, reliable, and cost-efficient LLM-powered systems in high-scale production environments. • Own evaluation strategy for complex, non-deterministic AI systems, including offline benchmarking, online experimentation, LLM-as-a-judge methodologies, and systematic failure analysis. • Proactively identify and mitigate agent failure modes such as hallucinations, tool misuse, retrieval errors, prompt brittleness, context drift, and multi-step reasoning breakdowns. • Define measurable quality standards (accuracy, faithfulness, task completion, latency, cost efficiency, robustness) and drive continuous system improvement. • Influence cross-team architecture decisions across ML, backend, and product engineering to ensure seamless integration of AI capabilities. • Mentor senior engineers, raise the technical bar, and contribute to long-term AI strategy and roadmap planning. • Translate cutting-edge research advances into practical, high-impact production systems.