MLOPs Engineer at Blackstraw Technologies in Remote

Role Description:

As a MLOps Engineer, you will collaborate with other Engineers, Development teams, Product Owners & Scrum Masters to realize critical business goals and be part of a team of smart, highly skilled technologists who are passionate about learning, data mining and prototyping cutting-edge technologies.

We develop RESTful API applications in Python and what we do is to help our research team to put ML models in production, so we work mostly in building Restful applications and training pipelines (mostly in AzureML), integrating inference codes, and providing tools and patterns for enhancing our MLOps cycle.

Responsibilities:

• Create and maintain a scalable infrastructure to deliver AI/ML processes, responding to the user requests in near real time.
• Develop RESTful Applications using Python.
• Design and implement the pipelines for training and deployment of ML models.
• Design dashboards to monitor a system, collect metrics, create alerts based on them and execute performance tests.
• Perform feasibility studies/analysis with a critical point of view and support & maintain (troubleshoot issues with data and applications).
• Contribute to architectural designs of large complexity and size, potentially involving several distinct software components.
• Mentoring other engineers fostering good engineering practices across the department.,
• Working closely with data scientists and a variety of end-users (across diverse cultures) to ensure technical compatibility and user satisfaction.
• Use distributed computing to validate and process large volumes of data to deliver insights.
• Evaluate technologies we can leverage, including open-source frameworks, libraries, and tools.
• Develop technical documentation for applications, including sequence diagrams, flowcharts and manuals.

Qualifications:

• 3+ years of applicable Mlops experience including strong programming in Python (and familiarity with Bash)
• Solid understanding of machine learning lifecycle (training, validation, deployment, monitoring)
• Experience with ML frameworks: PyTorch, TensorFlow, scikit-learn
• Model versioning, experiment tracking using MLflow / Weights & Biases
• Building and managing training & inference pipelines
• Model packaging, reproducibility, and rollback strategies
• CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI
• Hands-on experience with AWS / GCP / Azure
• Managing storage, compute, and networking for ML workloads
• Logging & observability using Prometheus, Grafana, ELK
  

Requesting only relevant profiles with immediate joining to apply.