DevOp/ML Ops Consultant

Key Responsibilities:
· Collaborate with data scientists and engineers to automate machine learning workflows, from data preparation and model training to deployment and monitoring.
· Implement continuous integration/continuous deployment (CI/CD) pipelines for machine learning systems.
· Monitor and manage the performance of deployed models, ensuring they remain accurate and efficient over time.
· Stay up-to-date with new technologies and advancements in the field of MLOps, introducing innovative solutions to improve existing processes.
· Develop best practices for model versioning, testing, and deployment to facilitate reproducibility and traceability within the ML lifecycle.

Technical Skills Required:
· Programming Languages: Proficiency in Python and familiarity with R, Java, or Scala.
· Machine Learning Frameworks: Experience with TensorFlow, PyTorch, Keras, or similar frameworks.
· Data Processing: Strong understanding of data processing and transformation techniques using tools like Pandas, NumPy, or Apache Spark.
· DevOps Tools: Knowledge of DevOps tools such as Docker, Kubernetes, Jenkins, GitLab CI/CD, and Ansible for automating deployment, scaling, and management of containerized applications.
· Cloud Platforms: Experience with cloud services (AWS) including compute instances, storage options, and managed services related to machine learning (e.g., AWS SageMaker).
· Configuration Management Tools: Proficiency in tools such as Ansible, Chef, or Puppet for automating software application deployment, configuration management, and infrastructure orchestration.
· Continuous Integration and Continuous Deployment (CI/CD): Experience with CI/CD tools like Jenkins, Travis CI, GitLab CI, or CircleCI.
· Machine Learning Operations (MLOps) Platforms: Understanding of MLOps principles and platforms to automate the deployment, monitoring, and management of machine learning models