Machine Learning Operations Engineer
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 MLOps Engineer will own the infrastructure that makes Propio’s AI products reliable, efficient, and secure. You’ll collaborate with AI engineers, prompt engineers, and product leads to automate workflows, monitor performance, and build resilient pipelines from experimentation to production.
This is a hands-on Engineering role with strategic impact.
Key Responsibilities:
- Design and implement ML model deployment pipelines (batch and real-time)
- Containerize models and build CI/CD workflows using tools like GitHub Actions, Docker, and Kubernetes
- Set up monitoring and alerting for model drift, latency, and failure rates
- Collaborate with AI Engineers to ensure production-readiness of LLM, ASR, and classification models
- Manage feature stores, model registries, and experiment tracking (e.g., MLflow, Weights & Biases)
- Enforce best practices in version control, reproducibility, and model lifecycle management
- Optimize inference performance for cost and speed, especially in streaming use cases
- Ensure compliance with privacy and security standards (HIPAA, PHI) for deployed AI systems
Requirements
Qualifications:
- 3+ years of experience in MLOps, DevOps, or ML Engineering
- Proficiency in Python, with experience building ML pipelines in tools like Airflow, Prefect, or FastAPI
- Strong experience with Docker, Kubernetes, and cloud platforms (e.g., Azure, AWS, or GCP)
- Familiarity with deploying NLP, LLM, or speech models in production
- Experience with observability tools (Prometheus, Grafana, or Datadog)
- Comfortable working in cross-functional teams with data scientists and product stakeholders
- Understanding of model security, bias mitigation, and audit logging in sensitive domains
Preferred Qualifications:
- Experience optimizing Whisper, OpenAI, or Hugging Face models for production
- Knowledge of vector databases, embedding servers, and retrieval-based systems (e.g., RAG pipelines)
- Prior work with hybrid cloud/on-prem setups or real-time streaming AI products
- Familiarity with model monitoring platforms like Arize, Fiddler, or Evidently
Average salary estimate
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