GCP Data Engineer

Pay Range: $60.00hr - $65.00hr Job Overview: We are seeking a highly skilled and experienced Data Engineer with a strong background in AI/ML to design, build, and optimize robust data pipelines and infrastructure on the Google Cloud Platform (GCP). The candidate will be passionate about leveraging data to power machine learning initiatives, ensuring data quality, accessibility, and scalability for advanced analytics and AI applications. Responsibilities: AI/ML Data Preparation: Collaborate closely with Data Scientists and Machine Learning Engineers to understand data requirements for model training, evaluation, and serving. Prepare, transform, and curate large, diverse datasets (structured, unstructured, streaming) to optimize them for AI/ML workloads. AI/ML Services: Utilize Gemini AI, AI Studio, and Vertex AI for model training, deployment, MLOps, Pipelines, Workbench, and AutoML. Create prompts and build applications using AI studio. Automation & MLOps: Automate data processes, develop CI/CD pipelines for data and ML models, and contribute to MLOps best practices for seamless deployment and monitoring of AI/ML solutions. Monitor, troubleshoot, and optimize the performance and cost-effectiveness of data pipelines and AI/ML infrastructure. Collaboration & Communication: Work effectively with cross-functional teams, including Data Scientists, Analysts, Software Engineers, and Product Managers, to understand data needs and deliver impactful solutions. Innovation & Research: Stay up-to-date with the latest advancements in data engineering, AI/ML, and GCP technologies, continuously exploring and recommending new tools and approaches. Qualifications: Bachelor's or Master's degree in Computer Science, Data Engineering, or a related quantitative field. Proven experience as a Data Engineer, with a strong focus on building data solutions for AI/ML applications. In-depth knowledge and hands-on experience with Google Cloud Platform (GCP) data services (BigQuery, Dataflow, Dataproc, Cloud Storage, Cloud Composer, etc.). Strong proficiency in programming languages such as Python (essential), and experience with Scala or Java is a plus. Expertise in SQL and experience with various database technologies (relational, NoSQL, data warehouses). Familiarity with machine learning concepts, algorithms, and workflows (e.g., feature engineering, model training, evaluation, deployment). Experience with machine learning frameworks like TensorFlow or PyTorch. Understanding of distributed systems, big data technologies, and real-time data processing. Experience with version control systems (e.g., Git) and CI/CD practices. Strong verbal and written communication skills. Google Cloud Professional Data Engineer certification is a strong plus. Cynet Systems