Description

Propio is on a mission to make communication accessible to everyone. As a leader in real-time interpretation and multilingual language services, we connect people with the information they need across language, culture, and modality. We’re committed to building AI-powered tools to enhance interpreter workflows, automate multilingual insights, and scale communication quality across industries.

We are hiring an Applied AI Engineer to build and deploy practical, high-impact AI systems. You will work across speech recognition, large language models, and prompt engineering to ship products like AI-generated summaries, interpreter QA tools, and multilingual retrieval systems. You'll operate at the intersection of engineering, research, and product with high ownership and startup-level pace.

This is a builder role, not a research-only position and you will be working closely with our VP of AI to get things live, fast.

Key Responsibilities

Prototype, build, and deploy end-to-end AI applications involving speech, LLMs, and text generation
Integrate APIs like OpenAI, Whisper, Deepgram, and open-source equivalents for ASR and NLP
Collaborate with Engineers to iterate and refine MVPs before transitioning to model-level optimization
Develop internal tools and dashboards to test summarization, QA scoring, and multilingual understanding
Rapidly test ideas and model variations to explore feasibility and impact (build-measure-learn loop)
Ensure model pipelines are robust, scalable, and ready for handoff to MLOps and production
Work with the AI PM to align technical outputs with business use cases and feedback loops
Stay current on applied AI trends in speech and LLMs, and advise on what to use vs. build

Requirements

Qualifications:

Master’s Degree in Engineering, preferably in Computer Science, Statistics, Data Science or equivalent work related experience
3–5+ years of experience working with NLP or speech models in real-world applications
Experience with Python, Hugging Face Transformers, OpenAI APIs, Whisper, LangChain, or similar frameworks
Experience deploying AI models in production or pilot environments (e.g., using FastAPI, Flask, or Streamlit)
Strong understanding of embeddings, prompt chaining, and pipeline orchestration
Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate)
Comfortable with rapid iteration, MVP mindset, and cross-functional collaboration
Prior exposure to multilingual or low-resource language challenges is a plus

Preferred Qualifications

Experience building speech-to-text pipelines or hybrid ASR + LLM systems
Experience with data labeling, annotation, or active learning workflows
Familiarity with real-time audio processing or latency-sensitive applications
Experience in healthcare, legal, or regulated environments (HIPAA, PHI, Section 1557)

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