Applied AI Engineer

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    "description_html": "<p><br></p><p>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.&nbsp;</p><p><br></p><p>We are hiring an <strong>Applied AI Engineer</strong> 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.</p><p><br>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.</p><p><br><strong>Key Responsibilities:&nbsp;</strong></p><ul><li>Prototype, build, and deploy end-to-end AI applications involving speech, LLMs, and text generation</li><li>Integrate APIs like OpenAI, Whisper, Deepgram, and open-source equivalents for ASR and NLP</li><li>Collaborate with Engineers to iterate and refine MVPs before transitioning to model-level optimization</li><li>Develop internal tools and dashboards to test summarization, QA scoring, and multilingual understanding</li><li>Rapidly test ideas and model variations to explore feasibility and impact (build-measure-learn loop)</li><li>Ensure model pipelines are robust, scalable, and ready for handoff to MLOps and production</li><li>Work with the AI PM to align technical outputs with business use cases and feedback loops</li><li>Stay current on applied AI trends in speech and LLMs, and advise on what to use vs. build</li></ul>",
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      "description": "<p>Description</p><p><br/></p><p>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. </p><p><br/></p><p>We are hiring an <strong>Applied AI Engineer</strong> 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.</p><p><br/>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.</p><p><br/><strong>Key Responsibilities: </strong></p><ul><li>Prototype, build, and deploy end-to-end AI applications involving speech, LLMs, and text generation</li><li>Integrate APIs like OpenAI, Whisper, Deepgram, and open-source equivalents for ASR and NLP</li><li>Collaborate with Engineers to iterate and refine MVPs before transitioning to model-level optimization</li><li>Develop internal tools and dashboards to test summarization, QA scoring, and multilingual understanding</li><li>Rapidly test ideas and model variations to explore feasibility and impact (build-measure-learn loop)</li><li>Ensure model pipelines are robust, scalable, and ready for handoff to MLOps and production</li><li>Work with the AI PM to align technical outputs with business use cases and feedback loops</li><li>Stay current on applied AI trends in speech and LLMs, and advise on what to use vs. build</li></ul><p>Requirements</p><p><br/></p><p><strong>Qualifications:</strong></p><ul><li>Master’s Degree in Engineering, preferably in Computer Science, Statistics, Data Science or equivalent work related experience </li><li>3–5+ years of experience working with NLP or speech models in real-world applications</li><li>Experience with Python, Hugging Face Transformers, OpenAI APIs, Whisper, LangChain, or similar frameworks</li><li>Experience deploying AI models in production or pilot environments (e.g., using FastAPI, Flask, or Streamlit)</li><li>Strong understanding of embeddings, prompt chaining, and pipeline orchestration</li><li>Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate)</li><li>Comfortable with rapid iteration, MVP mindset, and cross-functional collaboration</li><li>Prior exposure to multilingual or low-resource language challenges is a plus</li></ul><p><br/></p><p><strong>Preferred Qualifications:</strong></p><ul><li>Experience building speech-to-text pipelines or hybrid ASR + LLM systems</li><li>Experience with data labeling, annotation, or active learning workflows</li><li>Familiarity with real-time audio processing or latency-sensitive applications</li><li>Experience in healthcare, legal, or regulated environments (HIPAA, PHI, Section 1557)</li></ul><p>#LI-JS1 </p>",
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    "requirements_html": "<p><br></p><p><strong>Qualifications:</strong></p><ul><li>Master’s Degree in Engineering, preferably in Computer Science, Statistics, Data Science or equivalent work related experience&nbsp;</li><li>3–5+ years of experience working with NLP or speech models in real-world applications</li><li>Experience with Python, Hugging Face Transformers, OpenAI APIs, Whisper, LangChain, or similar frameworks</li><li>Experience deploying AI models in production or pilot environments (e.g., using FastAPI, Flask, or Streamlit)</li><li>Strong understanding of embeddings, prompt chaining, and pipeline orchestration</li><li>Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate)</li><li>Comfortable with rapid iteration, MVP mindset, and cross-functional collaboration</li><li>Prior exposure to multilingual or low-resource language challenges is a plus</li></ul><p><br></p><p><strong>Preferred Qualifications:</strong></p><ul><li>Experience building speech-to-text pipelines or hybrid ASR + LLM systems</li><li>Experience with data labeling, annotation, or active learning workflows</li><li>Familiarity with real-time audio processing or latency-sensitive applications</li><li>Experience in healthcare, legal, or regulated environments (HIPAA, PHI, Section 1557)</li></ul><p>#LI-JS1&nbsp;</p>",
    "requirements_text": "Qualifications:\n Master’s Degree in Engineering, preferably in Computer Science, Statistics, Data Science or equivalent work related experience\n 3–5+ years of experience working with NLP or speech models in real-world applications\n Experience with Python, Hugging Face Transformers, OpenAI APIs, Whisper, LangChain, or similar frameworks\n Experience deploying AI models in production or pilot environments (e.g., using FastAPI, Flask, or Streamlit)\n Strong understanding of embeddings, prompt chaining, and pipeline orchestration\n Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate)\n Comfortable with rapid iteration, MVP mindset, and cross-functional collaboration\n Prior exposure to multilingual or low-resource language challenges is a plus\n Preferred Qualifications:\n Experience building speech-to-text pipelines or hybrid ASR + LLM systems\n Experience with data labeling, annotation, or active learning workflows\n Familiarity with real-time audio processing or latency-sensitive applications\n Experience in healthcare, legal, or regulated environments (HIPAA, PHI, Section 1557)\n #LI-JS1"
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