Remote MLOps Consultant

Are you an MLOps expert with a strong background in designing, implementing, and automating MLOps workflows? Do you thrive in both hands-on development and leadership roles? Are you eager to upskill DevOps teams while building scalable infrastructures for ML models and AI agents? If so, we'd love to hear from you! As an MLOps Consultant, you will be responsible for creating and optimizing MLOps workflows, ensuring efficient model publishing, scaling, monitoring, and continuous improvement. Your work will play a key role in streamlining AI development and deployment processes. This is a full-time, home-based position, offering you the chance to work with a talented team of professionals from around the globe. Key Responsibilities: Design and implement automated, scalable, and reproducible MLOps pipelines. Collaborate with data scientists, machine learning engineers, and DevOps teams to align ML models with production systems. Develop and deploy CI/CD pipelines specifically tailored for machine learning models and AI agents. Design infrastructure to support AI agent workflows, including data ingestion, model orchestration, and multi-agent systems. Establish robust monitoring, logging, and alerting mechanisms for deployed models. Guide and train the DevOps team on MLOps frameworks, best practices, and tooling. Introduce and manage version control for data, models, and pipelines. Ensure compliance with security and governance requirements throughout the AI/ML lifecycle. Document processes and workflows to support knowledge transfer and long-term sustainability. Required Qualifications: 2+ years of proven experience in MLOps, including model deployment, monitoring, and automation. Strong background in DevOps practices and tools (e.g., Docker, Kubernetes, Terraform, CI/CD). Knowledge of CI/CD tools like Jenkins, GitLab CI/CD. Proficiency in machine learning lifecycle management tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Solid programming skills in Python and familiarity with ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Experience with the main cloud platforms, particularly with AWS Familiarity with data pipeline tools and frameworks, such as Apache Airflow or Kubeflow. Excellent communication skills to effectively mentor and collaborate with technical teams. Ability to assess, architect, and implement MLOps solutions tailored to business needs.