About the Role:
We are seeking a skilled and proactive AI Ops and ML Ops Software Engineer to join our team. This mid-level role is focused on designing, implementing, and managing operational pipelines and infrastructure for AI and machine learning models in production environments. You will work closely with data scientists, DevOps engineers, and software developers to ensure scalability, reliability, and efficiency in delivering AI-driven solutions.
Key Responsibilities:
•Build and manage CI/CD pipelines for deploying AI/ML models to production.
•Design and implement monitoring systems to track model performance, drift, and operational health.
•Automate model retraining, testing, and deployment processes.
•Collaborate with data scientists to ensure seamless integration of models into production systems.
•Optimize infrastructure for scalable and cost-effective machine learning operations.
•Troubleshoot and resolve issues in AI/ML pipelines and deployed systems.
•Implement robust version control and experiment tracking for models and datasets.
•Ensure compliance with security and data privacy regulations in model operations
Required Skills and Experience:
•3–5 years of experience in software engineering, with a focus on AI/ML Ops or DevOps.
•Proficiency in programming languages like Python, Java, or Go.
•Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
•Expertise in cloud platforms (e.g., AWS, Azure, GCP) and containerization tools like Docker and Kubernetes.
•Strong understanding of infrastructure as code (IaC) tools (e.g., Terraform, Ansible).
•Familiarity with data pipelines and orchestration tools (e.g., Airflow, Kubeflow, Prefect).
•Knowledge of model monitoring tools (e.g., Evidently AI, MLflow, SageMaker Monitor).
•Strong problem-solving and debugging skills.
•Understanding of CI/CD principles and tools (e.g., Jenkins, GitHub Actions).
