Machine Learning Engineer en Propio

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.

The Machine Learning Engineer will build, optimize, and deploy AI/ML models at scale to power Propio's language intelligence platform, with a focus on agentic AI workflows and conversational analytics. This role bridges research and production, ensuring models are robust, efficient, and seamlessly integrated into live systems that support both traditional ML tasks and complex agentic reasoning. 

Key Responsibilities:

• Develop and deploy production-grade ML models for translation, speech, interpretation, and agentic AI workflows
• Build and optimize inference pipelines for agentic systems that handle multi-step reasoning, tool integration, and domain-specific problem-solving (e.g., software engineering support, conversational analytics)
• Design scalable training and inference pipelines capable of supporting both traditional supervised learning tasks and LLM-based agentic systems 
• Implement model monitoring, evaluation, and continuous improvement workflows across diverse model types
• Collaborate with data engineering to ensure clean, structured, and compliant data for both traditional and agentic AI training 
• Partner with product and DevOps teams to integrate ML and agentic services into Propio's infrastructure
• Optimize models for latency, cost, and accuracy in real-time applications, including streaming speech and low-latency interpretation
• Work with Applied AI Engineering to transition agentic prototypes into production systems

Requirements

Qualifications:

• Bachelor's or Master's in Computer Science, AI, or related field
• 3+ years of experience in ML Engineer, DevOps, or ML Engineering
• Proficiency in Python, ML frameworks, and MLOps tools (MLflow, Kubeflow, SageMaker)
• Strong experience in software engineering skills (CI/CD, version control, testing, debugging production systems)
• Experience with cloud platforms (AWS, GCP, or Azure) 
• Experience with LLM APIs, prompt engineering, and agentic systems is a plus

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