Senior Machine Learning Engineer - MLOps & Platform Engineering (34107)
Posted Today by 1756883095
Negotiable
Outside
Remote
USA
Summary: We're seeking a seasoned Machine Learning Engineer with expertise in MLOps, platform development, and model engineering. The role involves designing automated workflows for ML model deployment, managing model lifecycles, and collaborating with data scientists to productionalize prototypes. The ideal candidate will have extensive experience in the Microsoft Azure ecosystem and familiarity with Databricks. This position is open to candidates located in Canada, with remote work options available.
Key Responsibilities:
- Design automated workflows for ML models from development to deployment.
- Develop robust CI/CD pipelines using Azure DevOps or GitHub Actions.
- Manage model lifecycle tasks such as versioning, monitoring, and retraining.
- Work within Databricks to develop feature pipelines and optimize model workflows.
- Integrate ML components with existing SaaS systems and help migrate models.
- Collaborate with data scientists to productionalize machine learning prototypes.
- Select algorithms and build scalable ML systems using Python frameworks.
- Tune and evaluate model performance over time.
Key Skills:
- 5+ years of experience in software, data, or DevOps engineering roles, with at least 3 focused on MLOps or ML engineering.
- Expertise in Python and key ML frameworks (TensorFlow, PyTorch, scikit-learn).
- Hands-on experience in the Microsoft Azure ecosystem (Azure ML, AKS, Azure DevOps).
- Deep familiarity with Databricks (Delta Lake, MLflow, Apache Spark).
- Understanding of infrastructure-as-code (e.g., Terraform), version control, and CI/CD practices.
- Strong communication skills and ability to work cross-functionally.
- Must be located in Canada and able to work with Canadian teams.
Salary (Rate): undetermined
City: undetermined
Country: USA
Working Arrangements: remote
IR35 Status: outside IR35
Seniority Level: undetermined
Industry: IT
We're currently seeking a seasoned Machine Learning Engineer who thrives at the intersection of data science, engineering, and operations. This role is ideal for someone who has not only mastered the craft of building and scaling ML solutions, but also knows how to put them into production, and keep them running smoothly.
What You'll Be Doing:
The role is a mix of MLOps, platform development, and hands-on model engineering. You'll be responsible for designing automated workflows that take ML models from development to deployment with minimal friction. This includes developing robust CI/CD pipelines (preferably using Azure DevOps or GitHub Actions) and managing model lifecycle tasks such as versioning, monitoring, and retraining.
A large portion of your time will be spent working within Databricks-developing feature pipelines, optimizing model workflows, and integrating ML components with existing SaaS systems. You'll also help migrate and scale models within an environment already hosting over 100 ML models, with another 25+ targeted for transition.
On the application side, you'll work closely with data scientists to productionalize machine learning prototypes. You'll select appropriate algorithms, build scalable ML systems using Python-based frameworks like TensorFlow and PyTorch, and help tune and evaluate performance over time.
The Ideal Candidate Brings:
- 5+ years of experience in software, data, or DevOps engineering roles-with at least 3 of those focused on MLOps or ML engineering.
- Expertise in Python, and confidence working with key ML frameworks (TensorFlow, PyTorch, scikit-learn).
- Hands-on experience working in the Microsoft Azure ecosystem-particularly Azure ML, AKS, and Azure DevOps.
- Deep familiarity with Databricks (including Delta Lake, MLflow, and Apache Spark) is non-negotiable.
- An understanding of infrastructure-as-code (e.g., Terraform), version control, and CI/CD practices in ML workflows.
- Strong communication skills and the ability to work cross-functionally across technical and business teams.
- You must be located in Canada and able to work with Canadian teams.
Additional:
- Certifications from Databricks or Microsoft (e.g., Azure Solution Architect).
- Experience working with LLM libraries or frameworks like transformers, trl, or deepspeed.
- Prior involvement in ML model migration projects or SaaS ML platform integration.
- A consulting background or client-facing role where clear communication and stakeholder management were key.