Senior Machine Learning Engineer

Takeaway•Edmonton, AB

About The Position

As a Senior ML Engineer, you will take a leadership role in shaping the strategic direction of our machine learning infrastructure, proactively identifying opportunities for innovation and improvement. You will drive the development of cutting-edge solutions that enhance the performance, scalability, and reliability of our machine learning systems. In collaboration with the Data Science team, you will ensure that models are deployed seamlessly, optimized for production environments, and meet the highest standards of efficiency and accuracy at scale. You will also be responsible for architecting and overseeing the development of complex machine learning pipelines, managing both real-time and batch inference systems that are integral to our predictive platforms. You will work closely with cross-functional teams to anticipate future needs, recommending and implementing long-term solutions that align with our business objectives. As a senior member of the team, you will mentor and guide mid-level Machine Learning Engineers, providing technical leadership and helping to shape best practices across the organisation. Your expertise will play a key role in conducting comprehensive code reviews, identifying areas for improvement, and fostering a culture of continuous learning and collaboration. Beyond technical execution, you will be expected to communicate effectively with both technical and non-technical stakeholders, translating complex machine learning concepts into actionable business insights. You will present innovative solutions to key stakeholders, making compelling cases for new initiatives and guiding the strategic direction of our machine learning efforts. Finally, you will lead the integration of new technologies and tools into our infrastructure, staying ahead of industry trends and ensuring that our machine learning frameworks remain at the cutting edge of technological advancements.

Requirements

Expert-level proficiency with cloud technologies (ideally AWS).
Extensive experience with containerization and orchestration (preferably Kubernetes).
Deep understanding of software development, DevOps, and MLOps best practices, with a proven track record of applying them in production.
Extensive experience in designing, deploying, and maintaining scalable models and services in production environments.
Strong understanding of Machine Learning, with the ability to collaborate deeply with Data Scientists on model deployment and optimization.
Experience with MLflow for experiment tracking and model management.
Experience with Airflow or Dagster for orchestrating end-to-end ML pipelines and workflows.
Significant experience with Data Engineering, Kafka, and stream processing.
Proficiency in Python and SQL.

Nice To Haves

Proficiency in any other programming languages is a strong plus.

Responsibilities

Take a leadership role in shaping the strategic direction of our machine learning infrastructure.
Proactively identify opportunities for innovation and improvement in machine learning infrastructure.
Drive the development of cutting-edge solutions to enhance the performance, scalability, and reliability of machine learning systems.
Ensure models are deployed seamlessly, optimized for production environments, and meet high standards of efficiency and accuracy at scale, in collaboration with the Data Science team.
Architect and oversee the development of complex machine learning pipelines.
Manage real-time and batch inference systems integral to predictive platforms.
Work closely with cross-functional teams to anticipate future needs and recommend/implement long-term solutions aligned with business objectives.
Mentor and guide mid-level Machine Learning Engineers, providing technical leadership and shaping best practices.
Conduct comprehensive code reviews, identify areas for improvement, and foster a culture of continuous learning and collaboration.
Communicate effectively with technical and non-technical stakeholders, translating complex machine learning concepts into actionable business insights.
Present innovative solutions to key stakeholders and make compelling cases for new initiatives.
Guide the strategic direction of machine learning efforts.
Lead the integration of new technologies and tools into the infrastructure.
Stay ahead of industry trends and ensure machine learning frameworks remain at the cutting edge.