Full-Time Machine Learning Engineer

We are seeking a highly skilled Machine Learning Engineer with a strong focus on Machine Learning Operations (MLOps) to join our innovative data science team.

As a Machine Learning Engineer, you will be responsible for designing, developing, and deploying scalable machine learning models into production environments. You will work closely with data scientists and software engineers to optimize model deployment pipelines, ensure continuous integration/continuous delivery (CI/CD), and maintain model performance in a live setting.

Day to day:

• Design, implement, and maintain machine learning pipelines for model training, validation, and deployment.
• Automate end-to-end model lifecycle management, including data preprocessing, model training, testing, monitoring, and updates.
• Collaborate with data engineering teams to build scalable, resilient, and secure infrastructure for ML models in production.
• Ensure CI/CD practices for model deployment, including version control, testing, and rollback strategies.
• Monitor model performance, identify bottlenecks, and implement improvements to maintain optimal results.
• Develop tools and frameworks for the rapid deployment and iteration of machine learning models.
• Optimize resource usage and cost by ensuring efficient model inference and serving architectures.
• Maintain and improve data pipelines, ensuring data quality, availability, and integrity.
• Collaborate with cross-functional teams to understand business needs and translate them into actionable ML solutions.
• Ensure compliance with data privacy and security standards in model handling and deployment.
    
• Proficiency in Python, TensorFlow, PyTorch, or other relevant ML libraries.
• Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud for ML deployment.
• Strong understanding of CI/CD pipelines, containerization (Docker, Kubernetes), and orchestration tools.
• Experience with monitoring tools like Prometheus, Grafana, or similar to track model performance.
• Knowledge of infrastructure-as-code tools like Terraform or CloudFormation.
• Experience with version control (Git) and workflow automation.
• Familiarity with distributed data systems like Spark, Hadoop, or Kubernetes.
• Strong problem-solving skills and a commitment to continuous learning.
• Excellent communication skills, both written and verbal.

Nice to have:

• Experience with A/B testing and model validation techniques.
• Familiarity with reinforcement learning and deep learning techniques.