Details on tech stack 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). Nice to have requirements to the candidate Strong leadership and mentorship capabilities, guiding teams toward best practices and high-quality deliverables. Excellent problem-solving skills, with a focus on designing efficient, high-performance systems. Effective project management abilities to handle multiple initiatives and ensure timely delivery. Strong emphasis on collaboration and teamwork , fostering a positive and productive work environment.
