- GCP Services: BigQuery, Cloud Dataflow, Pub/Sub, Dataproc, Cloud Storage.
- Data Processing: Apache Beam (batch/stream), Apache Kafka, Cloud Dataprep.
- Programming: Python, Java/Scala, SQL.
- Orchestration: Apache Airflow (Cloud Composer), Terraform.
- Security: IAM, Cloud Identity, Cloud Security Command Center.
- Containerization: Docker, Kubernetes (GKE).
- Machine Learning: Google AI Platform, TensorFlow, AutoML.
- Certifications: Google Cloud Data Engineer, Cloud Architect (preferred).
- Proven ability to design scalable and robust AI/ML systems in production, with a focus on high-performance and cost-effective solutions.
- Strong experience with cloud platforms (Google Cloud, AWS, Azure) and cloud-native AI/ML services (e.g., Vertex AI, SageMaker).
- Expertise in implementing MLOps practices, including model deployment, monitoring, retraining, and version control.
- Strong leadership skills with the ability to guide teams, mentor engineers, and collaborate with cross-functional teams to meet business objectives.
- Deep understanding of frameworks like TensorFlow, PyTorch, and Scikit-learn for designing, training, and deploying models.
- Experience with data engineering principles, scalable pipelines, and distributed systems (e.g., Apache Kafka, Spark, Kubernetes).
Essential functions
Nice to have requirements to the candidate
- Strongleadershipandmentorshipcapabilities, guiding teams toward best practices and high-quality deliverables.
- Excellentproblem-solvingskills, with a focus on designing efficient, high-performance systems.
- Effectiveproject managementabilities to handle multiple initiatives and ensure timely delivery.
- Strong emphasis oncollaborationandteamwork, fostering a positive and productive work environment
