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במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
מלאה
35,000-55,000 ₪
Who We're Looking For - The Dream Maker Were seeking a visionary and hands-on Senior data & ML Engineer to join our new data team, tasked with powering the "brain" behind our flagship product. This is a unique opportunity to build cutting-edge AI/ML systems from scratch, driving innovation at the intersection of data engineering, Machine Learning, and production-grade operations. The ideal candidate is a technical powerhouse, a blend of data Engineer, ML Engineer, and MLOps specialist who thrives on designing, building, and deploying scalable, robust AI solutions end-to-end. You'll collaborate closely with data engineers, data scientists, and analysts to turn raw data into production-ready models. As such, you'll play a key role in shaping and managing the entire Machine Learning lifecycle from data preparation and feature pipelines to model deployment, retraining, monitoring, and everything in between. This is not a role where youre handed a spec - this is a role where you design the blueprint. Our mission is to deliver an intelligent, impactful product that redefines our industry, and youll be the technical cornerstone making it happen. Our ultimate goal is to equip our clients with resilient safeguards against chargebacks, empowering them to safeguard their revenue and optimize their profitability. Join us on this thrilling mission to redefine the battle against fraud. Your Arena
* Design and build scalable, high-performance data pipelines and infrastructure to support AI/ML workflows from inception to production.
* Architect end-to-end ML systems, including data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
* Collaborate with data scientists to productionize Machine Learning models, ensuring seamless integration into our flagship product.
* Implement and optimize data preprocessing, feature stores, and retraining pipelines to keep models accurate and adaptive.
* Leverage Python, AWS, and Temporal to create robust, cloud-native solutions tailored to Real-Time and batch processing needs.
* Ensure data quality, scalability, security, and performance across all systems, from raw inputs to model outputs.
* Drive the adoption of MLOps best practices, including CI/CD for ML, model versioning, and automated retraining workflows.
* Troubleshoot and resolve complex issues in production AI/ML systems, ensuring reliability and efficiency.
* Evaluate and integrate cutting-edge tools, frameworks, and methodologies to enhance our AI infrastructure.
* Design and build scalable, high-performance data pipelines and infrastructure to support AI/ML workflows from inception to production.
* Architect end-to-end ML systems, including data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
* Collaborate with data scientists to productionize Machine Learning models, ensuring seamless integration into our flagship product.
* Implement and optimize data preprocessing, feature stores, and retraining pipelines to keep models accurate and adaptive.
* Leverage Python, AWS, and Temporal to create robust, cloud-native solutions tailored to Real-Time and batch processing needs.
* Ensure data quality, scalability, security, and performance across all systems, from raw inputs to model outputs.
* Drive the adoption of MLOps best practices, including CI/CD for ML, model versioning, and automated retraining workflows.
* Troubleshoot and resolve complex issues in production AI/ML systems, ensuring reliability and efficiency.
* Evaluate and integrate cutting-edge tools, frameworks, and methodologies to enhance our AI infrastructure.
Requirements:
What It Takes - Must haves: 5+ years of combined experience in data engineering, ML engineering, and/or MLOps, with a proven track record of building production AI/ML systems.
* Expert-level proficiency in Python and software engineering principles (e.g., modular design, testing, optimization).
* Extensive hands-on experience with AWS (e.g., S3, Lambda, ECS, SageMaker) and cloud-native architectures.
* Deep expertise in data pipelines, ETL processes, and feature engineering for Machine Learning.
* Strong knowledge of relational (e.g., PostgreSQL) and NoSQL databases, data warehouses, and feature stores.
* Experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and deploying models in production.
* Proficiency in orchestration tools (e.g., Temporal, Airflow) and building scalable workflows.
* Solid understanding of CI/CD pipelines, containerization (e.g., Docker), and infrastructure-as-code principles.
* Expertise in data validation (e.g., Great Expectations) and testing frameworks (e.g., Pytest). Advantages
* Experience with Temporal for workflow orchestration or similar tools (e.g., Flyte, Argo).
* Familiarity with modern data stack tools (e.g., DBT, DuckDB) and stream processing (e.g., Kafka, Flink).
* Hands-on experience with MLOps platforms (e.g., MLflow, Kub
What It Takes - Must haves: 5+ years of combined experience in data engineering, ML engineering, and/or MLOps, with a proven track record of building production AI/ML systems.
* Expert-level proficiency in Python and software engineering principles (e.g., modular design, testing, optimization).
* Extensive hands-on experience with AWS (e.g., S3, Lambda, ECS, SageMaker) and cloud-native architectures.
* Deep expertise in data pipelines, ETL processes, and feature engineering for Machine Learning.
* Strong knowledge of relational (e.g., PostgreSQL) and NoSQL databases, data warehouses, and feature stores.
* Experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and deploying models in production.
* Proficiency in orchestration tools (e.g., Temporal, Airflow) and building scalable workflows.
* Solid understanding of CI/CD pipelines, containerization (e.g., Docker), and infrastructure-as-code principles.
* Expertise in data validation (e.g., Great Expectations) and testing frameworks (e.g., Pytest). Advantages
* Experience with Temporal for workflow orchestration or similar tools (e.g., Flyte, Argo).
* Familiarity with modern data stack tools (e.g., DBT, DuckDB) and stream processing (e.g., Kafka, Flink).
* Hands-on experience with MLOps platforms (e.g., MLflow, Kub
This position is open to all candidates.
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
