Hiring For MLops Engineer

Required Skills

• Demonstrable proficiency in Python programming for data science applications.
• Proven experience designing, building, and maintaining MLOps pipelines to manage the end-to-end machine learning lifecycle.
• Expertise in containerization using Docker and orchestration with Kubernetes environments.
• In-depth knowledge of CI/CD methodologies utilizing tools like Jenkins, GitHub Actions, GitLab CI, or Azure DevOps for automated deployments.
• Mastery of model management platforms such as MLflow, Kubeflow, Airflow, or comparable solutions for workflow automation and lifecycle tracking.
• Strong familiarity with major cloud providers' AI/ML services including AWS SageMaker, Azure Machine Learning, and Google Vertex AI.
• Solid understanding of REST API development, model serving architectures, automated testing frameworks, and scalable deployment strategies.
• Versatility in using Git version control, Linux server administration, and infrastructure-as-code tools like Terraform or Ansible.
• Ability to implement robust systems for model monitoring, ensuring data quality, detecting and correcting model drift, and optimizing performance metrics.

Good to Have

• Practical experience with feature stores and centralized model registries.
• Working knowledge of deep learning frameworks including PyTorch, TensorFlow, or Scikit-learn.
• Emerging expertise in LLMOps, Generative AI (GenAI) deployment strategies, and LLM monitoring protocols.
• Broad familiarity with big data ecosystems using Spark, Kafka for stream processing, and handling large-scale datasets.
• Insight into security best practices, governance frameworks, and compliance requirements specific to production-grade ML systems.