Starboard Recruitment, on behalf of our client, is searching for a talented Lead Developer - Cloud Infrastructure (Remote in Canada).
Our client is one of Vancouver's most exciting tech startups. Founded in 2017 (currently ~75 person headcount), their platform has changed the way mining companies communicate with their stakeholders through the use of 3D visualization and AI tools. Already adopted by many of the top mining companies in the world and with a quickly growing list of customers, the company continues to scale. In 2023, they raised a successful multi-million dollar Series-A funding round - a reflection of their product's traction in the market. This year has perhaps been the most exciting year as the team have hit a large inflection point with their latest AI offering.
Apply today and our team will reach out to qualified candidates to discuss in further detail + provide the full job description.
Job Summary
As the Lead Developer - MLOps, you will lead the charge in developing and refining our machine learning operations platform, a vital component for deploying, managing, and scaling machine learning models in production.
In this position, you will design, implement, and optimize automated pipelines for model training, validation, deployment, monitoring, and evaluation. You will oversee the entire lifecycle of machine learning models, from experimentation to production, ensuring best practices in version control, testing, and CI/CD are followed.
The ideal candidate has a strong expertise in MLOps, cloud-based machine learning platforms, and automation frameworks. Additionally, you will play a key role in mentoring and guiding other developers, nurturing a culture of continuous improvement, technical excellence, and innovation in machine learning operations.
Responsibilities
- Create and manage automated pipelines for model training, validation, deployment, monitoring, and evaluation, ensuring smooth integration into production environments.
- Supervise the deployment of machine learning models into production, ensuring they are scalable, reliable, and optimized for performance across various environments.
- Implement robust monitoring systems to track model performance and detect data drift in production, while establishing procedures for regular model updates and upkeep.
- Design and enhance cloud infrastructure to support the entire machine learning lifecycle, from data ingestion and processing to model training and deployment.
- Define and enforce best practices for managing the model lifecycle, ensuring reproducibility, scalability, security, and adherence to industry standards.
- Keep up with the latest developments in MLOps tools, techniques, and frameworks, leading the adoption of new tools and technologies to enhance the platform’s capabilities.
- Collaborate closely with data scientists, software developers, geologists, and product managers to turn complex machine learning models into scalable, production-ready solutions.
Requirements
- BSc, MSc, PhD in Computer Science, Engineering, or a related field, or equivalent hands-on experience in ML engineering or MLOps roles.
- Over 2 years of direct experience in MLOps, including managing machine learning systems in a production environment.
- More than 3 years of experience with popular machine learning frameworks such as PyTorch, PyTorch Lightning, TensorFlow, JAX, or scikit-learn.
- Extensive knowledge of widely-used machine learning architectures like transformers and vision transformers, as well as techniques such as decision forests and clustering.
- Over 6 years of software development experience in at least one high-level programming language, such as Python, Go, or Java.
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