MLOps Engineer

Key Responsibilities :

- Develop and maintain CI/CD pipelines for machine learning models.

- Automate model deployment, monitoring, and scaling processes.

- Implement and manage version control for code, data, and models.

- Ensure data quality, security, and compliance throughout the ML lifecycle.

- Collaborate with data scientists, engineers, and other stakeholders.

- Optimize infrastructure for ML workloads.

- Troubleshoot and resolve issues in production ML systems.

- Implement logging, monitoring, and alerting for ML pipelines.

- Manage and optimize cloud resources for ML/AI workloads.

- Facilitate knowledge sharing and best practices across teams.

- Mentor peer developers on MLOps / AIOps and DevOps.

- Automate workflows for multi-model deployment on servers and embedded systems.

- Develop and manage APIs for serving multiple deep learning models efficiently using Flask, FastAPI, or similar.

- Optimize and convert models for embedded/PC/Android/Server deployment (e.g., TFLite, ONNX, .NEF) with quantization and pruning.

Required Technical Competencies :

- Programming languages : Python or similar.

- ML frameworks : TensorFlow, PyTorch, scikit-learn.

- Cloud platforms : AWS (preferred), Azure, or GCP.

- DevOps tools : Docker, Kubernetes, Jenkins, GitLab CI, Azure DevOps.

- Infrastructure as Code : Terraform, Ansible, CloudFormation.

- Big data technologies : Spark, Hadoop, Kafka.

- Monitoring and logging tools : ELK stack, Prometheus, Grafana.

- Version control : Git.

- Database management : SQL and NoSQL databases.

- Data pipeline tools : Airflow, Kubeflow or similar.

- CI/CD methodologies and tools for ML workflows.

- Model serving : Flask, FastAPI, TorchServe, TensorFlow Serving.

- Understanding of ML algorithms and model performance metrics.

- Knowledge of data privacy and security best practices.

- Familiarity with MLOps principles and tools : MLflow, DVC, Weights & Biases.