Senior AI / Machine Learning Engineer (NLP, OCR)

The HRS PAY business unit is hyper-focused on driving not only the HRS “flywheel” via the powerfulcombination of STAY and PAY, but also our own PAY flywheel whereby both corporate clients andthird-party partners benefit from leveraging HRS PAY’s technology and capabilities. The morecorporate clients use PAY, the more third-party partners such as banks, card issuers, ERPs, bookingplatforms and other financial service providers want to partner and collaborate with us, and thisdrives further commercial growth for PAY by monetizing those partnership streams. HRS PAY is a“tech fin” in that we provide the technology that powers the payment workflow as well as deliversthe highest level of available payment data to enable our customers to make intelligent decisionsaround their program spending. Only HRS PAY offers this powerful combination of automation, dataoptimization, and payment on-the-go technology all in one platform.
CHALLENGE
Work with a wide range of systems, processes, and technologies to own and solve problems from end-to-end.
Assist in building back-end infrastructure, data pipelines, and ML models for our products.
Experiment with ML techniques to create and test new program features.
Assist in extending and improving existing ML models.
Work alongside data engineers to create data and model pipelines and embed AI and analytics into the business decision processes.
Integrate ML models to end-users and run experiments.
Run tests, perform statistical analysis, interpret test results, and perform tuning and scaling.

Bachelor's degree or higher in computer science, mathematics, or a related field.
Experience with DevOps, MLOps, or LLMOps tools and methodologies, such as Git, Docker, Kubernetes, Jenkins, Airflow, Kubeflow, etc.
Proficiency in Python, SQL.
Solid understanding of fundamental statistics, probability theory, and the scientific method.
Experience in deploying machine learning models to production environments, including containerization (e.g., Docker) and orchestration tools (e.g., Kubeflow, MLflow), for batch and real-time inference.
Strong software engineering principles, including modular code design, code optimization, and familiarity with software development best practices.
Practical working experience with Azure cloud technologies (Azure AI) is a plus.
Experience with MLOps to build production-ready pipelines.
Strong English verbal and written communication skills, with a high level of comfort explaining complex technical topics to stakeholders.