We are seeking a versatile AI Language Engineer to design, build, and enhance natural language systems that power intelligent products and experiences, spanning both text and speech domains. This role combines linguistic insight, applied NLP expertise, and AI engineering execution to advance language understanding, generation, and evaluation across real-world applications. The AI Language Engineer will collaborate with product and engineering teams to translate language challenges into scalable AI solutions.

Design, develop, and refine large language model (LLM) workflows, including context engineering, prompt design, and evaluation frameworks to steer and improve model behaviors.

Build language processing components for features such as intent detection, entity recognition, summarization, retrieval-augmented generation (RAG), and conversational response quality.

Develop speech-to-text (ASR) and text-to-speech (TTS) workflows and evaluation frameworks, bridging audio-feature/signal-level processing with LLM-driven reasoning and orchestration.

Fine-tune and evaluate models using quantitative and qualitative metrics to ensure robust performance across tasks.

Analyze model outputs and conversational data to identify patterns, gaps, and failure modes, translating findings into actionable improvements.

Define and apply linguistic evaluation criteria to ensure tone, clarity, intent understanding, and contextual accuracy.

Experiment with prompt structures, retrieval strategies, and linguistic patterns to improve accuracy and robustness.

Drive R&D-style exploration on cutting-edge speech and language systems where best practices are still emerging, rapidly prototyping novel approaches and validating them through rigorous experimentation.

Lead data preprocessing, annotation, and language dataset creation, building reliable training and evaluation corpora.

Design experiments to test model adaptations and new techniques, tracking performance and iterating based on data insights.

Collaborate with software developers to integrate language models into production systems and ensure scalable deployment.

Build tooling for model evaluation, monitoring, and continuous improvement pipelines.

Extend evaluation and monitoring tooling to support large-scale, automated speech quality measurement for TTS and ASR in offline tests and production.

Support performance optimization, model serving architecture, and infrastructure integration.

Partner with product managers, conversation designers, UX researchers, and stakeholders to connect language capabilities with business objectives.

Serve as the NLP & language subject-matter expert within multidisciplinary teams.

Document methodologies, evaluation findings, best practices, and language guidelines to promote shared knowledge and reproducible workflows.

Present results and recommendations clearly to internal and external stakeholders.