Senior Machine Learning Engineer (Python)

TL;DR

Senior Machine Learning Engineer (Python): Building and optimizing end-to-end ML pipelines for production environments with an accent on feature engineering, model training, and scalable data workflows. Focus on converting data science prototypes into maintainable solutions using Snowflake, dbt, and AWS while ensuring system reliability through automated testing and CI/CD.

Location: Must be based in the USA, Canada, or Europe

Company

hirify.global is a technology services company specializing in digital transformation for the life sciences and healthcare sectors.

What you will do

• Design and build end-to-end ML pipelines including feature engineering, training, and batch inference.
• Convert data science prototypes into reliable, production-ready software solutions.
• Develop configurable ML frameworks using reusable components and parameter-driven workflows.
• Manage data preparation and transformation workflows within Snowflake and dbt Cloud.
• Implement automated testing, versioning, monitoring, and CI/CD processes.
• Collaborate with data scientists, engineers, and stakeholders to integrate model outputs into business systems.

Requirements

• 6–8 years of experience in machine learning, software, or data engineering.
• Strong Python software engineering skills with experience in object-oriented design.
• Hands-on experience with traditional ML libraries (pandas, NumPy, scikit-learn, PyTorch).
• Proficiency in SQL, Snowflake, dbt Cloud, and AWS.
• Experience with Docker and Git-based CI/CD workflows.
• Understanding of production support, model monitoring, and reproducibility.

Nice to have

• Experience with scheduled batch prediction pipelines and configurable frameworks.
• Familiarity with Kubernetes, workflow orchestration, or infrastructure as code.
• Exposure to generative AI or LLM-based applications.
• Domain experience in pharmaceutical, biotechnology, or healthcare environments.

Culture & Benefits

• Competitive compensation packages.
• Flexible working hours to support work-life balance.
• Continuous education, mentoring, and professional development programs.
• Opportunity to work with a team possessing deep technical expertise.
• Contract-based engagement with potential for extension.