Machine Learning Operations (MLOps) Engineer

TalentorAI is partnering with an innovative company in the AI and technology space to find an experienced MLOps Engineer. This is a fantastic opportunity to join a fast-growing company focused on leveraging machine learning and artificial intelligence to drive impactful solutions in the industry. As an MLOps Engineer, you’ll play a key role in deploying, optimizing, and maintaining machine learning models in production environments, ensuring they are scalable, efficient, and performant.

•  Model      Deployment & Automation: Lead the deployment and scaling of      machine learning models, ensuring their smooth operation in production.      Automate the machine learning lifecycle, including training, testing, and      deployment pipelines.
• Infrastructure      Management: Design and maintain robust infrastructure to support      machine learning models in a cloud-based environment (AWS, GCP, Azure).
• CI/CD      for ML: Implement and optimize continuous integration/continuous      deployment (CI/CD) pipelines for ML models, ensuring smooth transitions      from development to production.
• Model      Monitoring & Maintenance: Set up monitoring systems to track model      performance in production, identifying potential issues like model drift      and providing solutions to maintain high accuracy.
• Optimization      & Scaling: Improve deployment speed, model efficiency, and system      reliability to handle increasing amounts of data and traffic. 

•   Experience: 2+ years of experience in MLOps or similar roles, with a focus on deploying and maintaining machine learning models.
• Technical Expertise:
  • Proficiency with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
  • Experience in automating ML workflows using CI/CD tools (Jenkins, GitLab CI, CircleCI, etc.).
  • Strong coding skills in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
  • Knowledge of model monitoring tools like Prometheus or Grafana.
• Version Control: Experience with Git for collaborative development and version control.
• ML Workflow: Solid understanding of ML models, data pipelines, and the      operational challenges of scaling ML systems.
• Problem Solving: Strong analytical and troubleshooting skills with a focus on maintaining high-quality production systems.

• Experience with MLOps tools such as Kubeflow, MLflow, or Seldon.
• Familiarity with distributed systems, big data technologies (e.g., Apache Spark, Hadoop), or advanced model optimization techniques.
• Previous experience working within AI, data science, or a related field.
• Knowledge of ethical AI practices and model fairness.

• Competitive Salary: £60,000 - £70,000 based on experience. 
• Career Development: Opportunity to work with cutting-edge technologies and further develop your skills in a rapidly growing field. 
• Collaborative Culture: Join a dynamic, cross-functional team and work on impactful      AI solutions.
• Work-Life Balance: Enjoy a flexible work environment with opportunities for remote work and a healthy work-life balance.
• Impactful Work: Contribute to building high-performance AI systems that solve      real-world challenges in a fast-paced industry.