Senior Machine Learning Engineer

Responsibilities:

• Design and build end-to-end ML pipelines covering feature engineering, model training, backtesting, validation, reporting, and batch inference.
• Convert data science prototypes into reliable and maintainable production solutions.
• Develop a configurable ML framework using reusable components and parameter-driven workflows.
• Build and maintain Python-based ML workflows using an existing codebase.
• Support data preparation and transformation workflows in Snowflake and dbt Cloud.
• Implement data quality checks, automated testing, versioning, monitoring, and CI/CD.
• Integrate model outputs into downstream business systems and operational workflows.
• Contribute to architecture decisions and propose practical implementation approaches.
• Support production operations, troubleshooting, model refreshes, and continuous improvement.
• Collaborate with data scientists, data engineers, architects, and business stakeholders.

What we expect:

• 6–8 years of experience in machine learning engineering, software engineering, data engineering, or a related field.
• Strong Python software engineering skills, including experience with reusable and object-oriented code.
• Experience building production ML pipelines.
• Hands-on experience with traditional ML methods and libs (pandas, NumPy, scikit-learn, PyTorch).
• Hands-on experience with Docker.
• Strong SQL and data engineering skills.
• Practical experience with Snowflake and dbt Cloud.
• Experience working with AWS.
• Experience with Git-based CI/CD workflows, such as GitHub Actions.
• Understanding of testing, monitoring, versioning, reproducibility, and production support.
• Strong communication, ownership, troubleshooting, and problem-solving skills.

Nice to have:

• Experience with scheduled batch prediction pipelines.
• Experience building configurable frameworks that support multiple products or business units.
• Familiarity with CRM or other downstream business-system integrations.
• Familiarity with Kubernetes, workflow orchestration, or infrastructure as code.
• Exposure to generative AI or LLM-based applications.
• Experience in pharmaceutical, biotechnology, healthcare, or life sciences environments.

You will build and improve production ML pipelines and reusable ML infrastructure. The role focuses primarily on traditional ML and scheduled batch predictions. You will work with data scientists to productionize models, automate training and validation workflows, and integrate model outputs into downstream business systems. A key objective is to consolidate multiple similar pipelines into a single configurable framework that supports different products and use cases without duplicating code.

• Competitive compensation
• Flexible working hours
• Continuous education, mentoring, and professional development programs
• A team with an excellent tech expertise
• Contract through the end of the year, with a possible extension based on project needs and performance.