AI Engineering: Data & MLOps Engineer.  

• Build and manage scalable data pipelines for structured and unstructured data, ensuring data quality, integrity, and availability.
• Design, implement, and maintain MLOps workflows for training, testing, deploying, and monitoring machine learning models.
• Collaborate with data scientists and ML engineers to automate end-to-end machine learning lifecycle processes (CI/CD/CT for ML).
• Develop and manage feature stores, model registries, and metadata tracking tools.
• Ensure system reliability and performance using logging, monitoring, and alerting tools.
• Support infrastructure-as-code (IaC) and cloud automation for model training and inference environments.
• Integrate ML models into production systems using containers (Docker), orchestration tools (Kubernetes), and cloud services.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
• 3+ years of experience in data engineering, DevOps, or MLOps roles.
• Strong programming skills in Python, with experience in libraries such as Pandas, MLflow, Airflow, and PyTorch/TensorFlow.
• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and services such as S3, Lambda, SageMaker, Vertex AI, or Databricks.
• Familiarity with CI/CD tools (e.g., GitHub Actions, Jenkins, ArgoCD) and containerization (Docker, Kubernetes).
• Solid understanding of data formats, ETL frameworks, and scalable storage systems.

Preferred Qualifications:

• Experience with model versioning, A/B testing, and continuous training in production.
• Familiarity with modern data stack tools (e.g., dbt, Kafka, Snowflake, Redshift).
• Knowledge of security best practices in handling sensitive data and models.
• Certification in cloud technologies (e.g., AWS Certified Machine Learning, Google Cloud ML Engineer).