ML Ops Engineer (AI project)

ML Ops Engineer (AI project)

We are working on an exciting new project, started less than a year ago, aiming to bring a unique product to the artificial intelligence landscape. Our team is passionate about creating innovative machine learning solutions that make a real impact. Our platform will enable users and businesses to make use of AI without deep technical knowledge while also enabling hardware owners to get paid for sharing computing power. Join us and be part of a dynamic environment where your ideas and expertise can shape the future of how users and companies utilize AI. 

Responsibilities:

• Build & Maintain ML Pipelines: Design, develop, and optimize automated ML pipelines for model training, validation, deployment, and monitoring.
• CI/CD for ML Models: Develop and manage continuous integration and continuous delivery (CI/CD) pipelines to streamline the deployment of machine learning models.
• Model Monitoring & Performance Tuning: Monitor models in production for accuracy, drift, and performance, and optimize models and infrastructure to ensure reliability.
• Collaboration with Data Scientists: Work closely with data scientists to understand model requirements, improve data pipelines, and ensure seamless integration from research to production.
• Security & Compliance: Ensure the security of ML models and comply with regulations (e.g., GDPR) in handling sensitive data.
• Automation: Automate repeatable tasks in the ML lifecycle to enhance productivity and reduce errors.

Qualifications:

• 3+ years of experience in ML Ops, DevOps, or a similar role.
• Strong experience with cloud platforms, especially AWS
• Proficient in containerization (Docker, Kubernetes) and microservices architecture.
• Experience with orchestration tools (e.g., Airflow, Kubeflow).
• Solid understanding of version control and CI/CD tools (Git, Jenkins).
• Strong programming skills in Python or another language relevant to machine learning.
• Familiarity with machine learning frameworks (TensorFlow, PyTorch, scikit-learn).
• Ability to troubleshoot performance and security issues in machine learning systems.

Bonus skills:

• Experience with infrastructure-as-code tools (Terraform, Ansible).
• Knowledge of big data technologies (e.g., Spark, Hadoop).
• Familiarity with MLOps tools like Seldon, Tecton, or Fiddler.

Ready to Make an Impact? 

If you're excited about working on innovative machine learning solutions and want to be part of a passionate team, we'd love to hear from you! Apply now and let's shape the future of neural nets together.