Description Summary: M3 is embedding AI into its core product workflows to deliver intelligent automation and insights for the hospitality industry. We operate in a cloud-native environment, and AI solutions are expected to integrate seamlessly with modern application and platform architectures. The Senior ML / AI Engineer is responsible for designing, building, deploying, and maintaining production-grade AI and machine learning solutions, including predictive models, intelligent automation, and emerging LLM-enabled capabilities.
This role partners closely with internal engineering teams and external AI partners to establish scalable MLOps practices, ensure reliable model performance, and build internal AI/ML engineering capability. The position requires both strong applied AI/ML expertise and production engineering discipline.
Essential Duties:The duties listed below are the essential functions of this position, and they may change as the needs of the company demand. All associates are expected to do what is necessary to get the work done and to cooperate fully with their supervisor's requests for additional or altered duties.
- Design, build, and deploy machine learning models into production environments, including REST API-based inference services and batch or real-time scoring pipelines.
- Develop scalable training and inference pipelines using structured enterprise data.
- Establish and maintain MLOps practices including model versioning, monitoring, alerting, retraining workflows, and auditable model lifecycle management.
- Collaborate with Data Engineering to ensure AI-ready data structures and pipelines.
- Integrate ML outputs into product workflows in partnership with application engineering teams.
- Evaluate and prototype new AI use cases aligned to product strategy, selecting the appropriate approach (e.g., supervised learning, embeddings-based similarity, retrieval-driven architectures, or hybrid methods) based on problem context and data characteristics.
- Work alongside external AI partners initially and progressively transition ownership internally.
- Define performance benchmarks and ensure production reliability of deployed models.
- Document processes, best practices, and governance considerations for responsible AI usage.
Education/Training/Experience:- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or related field (or equivalent practical experience).
- 7+ years of experience in machine learning engineering or applied machine learning roles, with demonstrated experience delivering production-grade AI systems in enterprise environments.
- Strong programming skills in Python and experience with ML libraries (e.g., scikit-learn, PySpark, pandas).
- Experience deploying and maintaining ML models in production environments.
- Experience working in cloud-native environments (Azure preferred; AWS or GCP acceptable).
- Familiarity with MLOps tools such as MLflow or similar model lifecycle management platforms.
- Experience working with structured enterprise datasets.
- Experience evaluating and implementing both traditional machine learning approaches and embedding-based or retrieval-driven architectures (e.g., vector search, similarity matching) is a plus.
- Demonstrated ability to select the right technical solution for a given business problem rather than defaulting to a single modeling paradigm.
- Ability to collaborate effectively across engineering, product, and business teams.
