Cradlepoint is seeking a highly skilled and experienced Machine Learning Engineer to design, build, and deploy scalable machine learning systems in production. This is an engineering-focused role where you will partner with data scientists to productionize models, own ML pipelines end-to-end, and drive the reliability, automation, and performance of our critical ML infrastructure. You will work on mission-critical systems where robustness, monitoring, and maintainability are paramount, leveraging modern MLOps tools, cloud platforms, containerization, and model serving at scale.

What You Will Do: Key Responsibilities

Design and build robust ML pipelines and services for efficient model training, validation, and seamless deployment into production.
Collaborate closely with data scientists, solution architects, DevOps engineers, and other stakeholders to align components and pipelines with overarching project goals and requirements. Communicate any deviations from target architecture effectively.
Ensure seamless cloud integration with AWS and Azure cloud services for enhanced performance, scalability, and resource utilization.
Build reusable infrastructure components applying best practices from both DevOps and MLOps.
Adhere to stringent security standards and regulatory compliance, particularly when handling confidential and sensitive data within ML systems.
Design optimal network plans for given Cloud Infrastructure, ensuring compliance with established network security guidelines.
Monitor model performance in production and implement automated drift detection and retraining pipelines to maintain model accuracy and relevance.
Optimize models for performance, scalability, and cost efficiency through techniques such as batching, quantization, and hardware acceleration.
Create detailed documentation and guidelines for the effective use and modification of developed components, fostering knowledge sharing within the team.

Required Qualifications

Strong programming skills in Python.
Deep experience with major ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and XGBoost.
Hands-on experience with MLOps tools like MLflow, Airflow, TFX, Kubeflow, or BentoML.
Proven experience deploying models using Docker and Kubernetes.
Strong knowledge of cloud platforms (AWS/GCP/Azure) and their respective ML services (e.g., SageMaker, Vertex AI).
Proficiency with data engineering tools including Spark, Kafka, and both SQL/NoSQL databases.
Solid understanding of CI/CD (Continuous Integration/Continuous Delivery) principles, version control (Git), and infrastructure as code (Terraform, Helm).
Experience with monitoring and logging tools such as Prometheus, Grafana, and ELK stack.

Good-to-Have Skills

Experience with feature stores (e.g., Feast, Tecton) and experiment tracking platforms.
Knowledge of edge/embedded ML, model quantization, and optimization techniques.
Familiarity with model governance, security, and compliance considerations in ML systems.
Exposure to on-device ML or streaming ML use cases.
Experience leading cross-functional initiatives or mentoring junior engineers.