MLOps Engineer - Python

Responsibilities :

- Develop, optimize, and maintain scalable machine learning pipelines for training, evaluation, and deployment.

- Work with research and product teams to productionize AI/ML models, ensuring reliability, scalability, and performance.

- Automate workflows for data preprocessing, model training, validation, and monitoring.

- Implement robust monitoring systems for detecting data drift, model degradation, and system anomalies.

- Build APIs, services, and tools that integrate models seamlessly into end-user applications.

- Ensure reproducibility, experiment tracking, and version control using modern MLOps practices.

- Collaborate with engineers to optimize inference performance and reduce latency in production.

- Stay updated on best practices in applied ML, data engineering, and MLOps.

Requirements :

- Experience in building and deploying ML models into production.

- Strong proficiency in Python and ML frameworks (e. g., PyTorch, TensorFlow, Scikit-learn).

- Experience with data processing frameworks (e. g., Pandas, Spark) and scalable data pipelines.

- Hands-on experience with cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes).

- Familiarity with CI/CD pipelines and workflow orchestration tools (Airflow, Prefect, Dagster).

- Strong understanding of software engineering principles, APIs, and system design.

- Ability to debug, optimize, and scale ML workloads in production environments.

Bonus Points :

- Experience with monitoring/observability tools for ML systems (e. g., Evidently AI, Prometheus, Grafana).

- Exposure to vector databases, RAG pipelines, or real-time inference systems.

- Familiarity with MLflow, Kubeflow, or other experiment management platforms.

- Contributions to open-source ML/MLOps projects.