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The Platform Architect is a key team member shaping and supporting the enterprise data & machine learning platform. Certifications in Application Development, agile methods, machine learning, and relevant data platforms are advantageous.
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DescriptionThe Platform Architect will play a key role in building and supporting the company's core enterprise data and machine learning platform, and will be instrumental in our journey towards digital transformation leveraging AWS stack technologies.
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Experience with data modeling, data prep and machine learning tools like Alteryx, RapidMinder, RStudio and Tableau Prep. Experience with statistical and mathematical modeling, artificial intelligence and machine learning software and methods.
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Hands-on expertise building, deploying, and managing machine learning models on cloud platforms like Google Cloud (BigQuery, Vertex AI), AWS (SageMaker, Lambda), or Azure (Machine Learning Studio.
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Expertise in machine learning algorithms, deep learning frameworks (e.g., TensorFlow, PyTorch), and programming languages (e.g., Python, Java). Lead the design and development of advanced machine learning models to solve business challenges, leveraging techniques such as deep learning, reinforcement learning, natural language processing, and computer vision.
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Experience with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, etc. Strong understanding of machine learning concepts and algorithms, including supervised and unsupervised learning, deep learning, reinforcement learning, etc.
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Work with our business partners to identify requirements and architecture and implement scalable, open solutions using Cloud Native technologies, including and leveraging AWS services, Machine Learning techniques, GIT, Jenkins, Docker containerization and virtualization, and Terraform.
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Experience with cloud platforms for IoT and machine learning (e.g., AWS IoT, Azure IoT, Google Cloud IoT). - Design, develop, and implement machine learning models and algorithms from sensor, facility, and operations real-time data to optimize and improve the functionality of decisions addressing waste reduction, production optimization, predictive maintenance, and environmental impact.
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Familiarity with popular MLOps tooling from cloud vendors like GCP (Vertex AI), AWS (SageMaker) or Azure Machine Learning and MLFlow, Kubeflow, etc. With Kumo, we are building the first data platform to seamlessly allow machine learning over data warehouses for faster, simpler, and smarter predictions to combat data waste and maximize data value.
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Familiarity with a diverse set of technologies, including but not limited to Spark, Flink, Kafka, Iceberg, and Ray, in the data and machine learning ecosystem. 10 years of hands-on experience in the realm of large-scale data processing and/or machine learning infrastructure, showcasing a robust understanding of the complexities associated with such environments.
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Your deep understanding of Kubernetes architecture, cloud infrastructure, and your passion for AI and machine learning will be instrumental in guiding our team to success. We are passionate about making businesses secure and simplify security with zero compromise using AI and Machine Learning.
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Provide senior leadership across architecture, engineering, and operations for a leading automotive technology innovation company utilising AI driven and machine learning technologies to evolve solutions and fast-track innovation in the space.
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Familiarity with machine learning frameworks and data science workflows. Experience with cloud-based data platforms like AWS, Azure, or Google Cloud. Knowledge of data warehousing concepts and technologies like Snowflake or Redshift.
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Knowledge of software testing practices (test-driven development, automated test suites within a continuous integration framework); integrations experience using APIs such as REST and SOAP, JDBC connections, and web services; familiarity with Amazon Web Services (AWS), Artificial Intelligence (AI), Analytics, Machine Learning, Google Cloud, Application Integration, Database, Developer Tools, Management & Governance, and Elastic Containers is preferred.
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Equilibrium is composed of deeply knowledgeable industry experts across all our functions, with decades of experience in energy-specific commercial structuring, power systems engineering, machine learning, computational research, operations research, distributed and compute-intensive infrastructure, and modern software & ML engineering.
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