Regular Engineer (Data Engineer / MLOps)

Responsibilities:

Build and manage data pipelines to support AI model training and inferencing, ensuring data is efficiently processed and integrated.
Collaborate with ML engineers and data scientists to automate the model deployment process using CI/CD pipelines.
Develop monitoring systems to track the performance of AI models in production environments.
Ensure proper data preprocessing for AI/ML models, including cleaning, transforming, and validating data for model training.
Build tools to facilitate model versioning, automated retraining, and testing of optimized models.

Qualifications:

3+ years of experience in data engineering or MLOps, particularly with AI model deployment and automation.
Strong understanding of data pipeline management and cloud-based AI infrastructures.
Knowledge of best practices in MLOps for automating and monitoring machine learning workflows.

Technical Skills:

Data Engineering Tools: Proficiency in frameworks such sa Apache Kafka, Apache Spark, Airflow, and ETL frameworks for managing data pipelines.
MLOps Frameworks: Experience with MLflow, Kubeflow, or Seldon for automating model training, testing, and deployment.
Cloud Platforms: Expertise in cloud platforms like AWS SageMaker, Azure ML, and Google AI Platform for AI/ML infrastructure.
Containerization: Proficiency in using Docker and Kubernetes for deploying scalable AI models.
Monitoring & Logging: Familiarity with monitoring tools such as Prometheus, Grafana, or ELK Stack for tracking AI model performance in production.