At Niagara, we’re looking for Team Members who want to be part of achieving our mission to provide our customers the highest quality most affordable bottled water.
Consider applying here, if you want to:
• Work in an entrepreneurial and dynamic environment with a chance to make an impact.
• Develop lasting relationships with great people.
• Have the opportunity to build a satisfying career.
We offer competitive compensation and benefits packages for our Team Members.
Systems Development Engineer I (Generative AI tools)
The Systems Development Engineer – Level 1 is responsible for designing Generative AI tools particularly Large Language Models (LLM), and other AI tools aimed at automating project management. As a Level 1 Systems Development Engineer, you will work closely with cross-functional teams, including data scientists, engineers, and project managers, to build innovative AI/ML applications and integrate them into existing systems. This role extends to the development and optimization of Machine Learning Operations (ML Ops) for all Predictive Maintenance programs associated with Niagara's fixed assets, the establishment of ML Ops pipelines, and essential data engineering tasks.
Detailed Description
• ML and Statistical Model Design:
• Solution Design: Leverage AI technologies, such as Python-based machine learning libraries (e.g., TensorFlow, PyTorch, OpenAI, transformer), to streamline project planning, execution, and monitoring processes, with a specific focus on Large Language Models (LLM).
• Model Development: Develop, implement, and evaluate AI/ML models and algorithms. This includes data preprocessing, feature selection, model training, hyper-parameter tuning, prompt engineering, and performance evaluation.
• Data Management: AI/ML systems heavily rely on data. The AI/ML Engineer plays a crucial role in defining data requirements, designing data pipelines, and establishing data governance practices. They ensure data quality, security, and privacy considerations are addressed throughout the AI/ML lifecycle.
• Test and deploy machine-learning algorithms, publish results, and enhance models for accuracy.
• Documentation: Document project requirements, methodologies, and outcomes. Prepare technical reports, presentations, and user guides to effectively communicate AI/ML solutions to stakeholders.
• Research and Innovation: Stay updated with the latest advancements in AI/ML technologies, tools, and methodologies. Conduct research and experiments to explore new approaches and improve existing models.
• Data Acquisition:
• Develop automation frameworks that enable efficient data acquisition, post-processing, and test execution
• Develop requirements for data acquisition from manufacturing field assets
• Configure and test data tags into Edge computing platform and ensure stable connections.
• Support readiness reviews and validation to ensure efficient execution.
• Research opportunities for data acquisition and new uses for existing data
• Data Wrangling and Modelling:
• Develop data set processes for data modeling, mining and production
• Design, develop, and test logics to manipulate data for business requirements.
• Design and develop requirements to optimize storage of data using mathematical models.
• Application Development:
• Understanding requirements and how they translate to new application features
• Collaborating with development team and other IT staff to set specifications for new applications
• Design creative prototypes according to specifications.
• Perform unit and integration testing before launch, Conduct functional and non-functional testing
• Develop technical documents and handbooks to accurately represent application design and code
• Process Automation development:
• Collaborate with IT and Manufacturing Information Systems groups as a part of implementation of projects.
• Selecting features, building and optimizing automation processes using machine learning and data acquisition techniques
• Data mining using state-of-the-art methods
• Extending company’s data with third party sources of information when needed
• Enhancing data collection procedures to include information that is relevant for building analytic systems
• Processing, cleansing, and verifying the integrity of data used for analysis
• Doing ad-hoc analysis and presenting results in a clear manner
• Please note that this job description is not designed to contain a comprehensive list of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without prior notice.
• Systems Reliability Engineer is estimated to travel 10-30%
• Please note this job description is not a full list of activities, duties or responsibilities required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without prior notice.
Work Experience/KSA’s
• Required:
• 2-4 years – Experience i
Location: Diamond Bar, CA
Posted: Oct. 27, 2024, 9:40 p.m.
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