AI/ML Engineer II - Niagara Bottling

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.

AI/ML Engineer II

AI/ML Engineer–  is responsible for designing Generative AI tools particularly Large Language Models (LLM), and other AI tools aimed at automating project management. As an AI/ML 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 in Python, R or another Programming language
  • 2-4 years – Experience with TensorFlow, PyTorch, or scikit-learn
  • 2+ year(s) – Experience in Industrial ML/Automation/Data Science or other related fields
  • 2+ year(s) – Experience with cloud computing platforms such as AWS, Azure, or GCP
  • 2+ year(s) – Experience with natural language processing (NLP)
  • 2+ year(s) – Experience with Deep learning, Compute Vision, Reinforcement learning
  • 2+ year(s) – Experience with ethical considerations in machine learning

*Experience may include a combination of work experience and education

• Preferred:
  • 3-5 years – Experience in Python, R or another Programming language
  • 3-5 years – Experience with TensorFlow, PyTorch, or scikit-learn
  • 2-4 years – Experience in Industrial ML/Automation/Data Science or other related fields
  • 2-4 years – Experience with cloud computing platforms such as AWS, Azure, or GCP
  • 2-4 years – Experience with natural language processing (NLP)
  • 2-4 years – Experience with Deep learning, Compute Vision, Reinforcement learning
  • 2-4 years – Experience with ethical considerations in machine learning

*Experience may include a combination of work experience and education

Preferred Competencies and Skills

• Proficiency in Azure ML studio and related tools for model development, deployment, and monitoring.
• Proficiency in using query languages such as SQL, Hive, Pig. Etc.
• Proficiency in, but not limited to:
  • Microsoft Office Applications – Word, Excel, PowerPoint, Outlook, Project, Visio, etc.
• Proficiency in applied statistical skills, such as distributions, statistical testing, regression, etc.
• Basic understanding of data acquisition and processing tools and techniques and developing algorithms on common platforms to generate outputs
• Basic scripting and programming skills such as Java Script, C++, Python and C#
• Basic understanding of PLC/SCADA systems such SIEMENS S7, ALLEN BRADLEY, BnR, Edge data Management etc.
• Basic understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
• Preferred experience with common data science toolkits, such as R, Weka, Python with focus on NumPy, Matplotlib and Pandas, MATLAB, etc.
• Able to translate data into recommendable actions to senior management
• Strong analytical and problem-solving skills
• Self-motivated with a proven record of taking initiative
• Able to work with minimal supervision
• Detail-oriented with excellent oral and written communication skills
• Able to execute tasks in a very dynamic and ever-changing environment

Education

• Minimum Required: 
  • Bachelor's Degree in Computer/Industrial/Automation/Data Science Engineering or other related fields or equivalent experience

• Preferred: 
  • Master's Degree in Computer/Industrial/Automation/Data Science Engineering

Typical Compensation Range

$96,309.20 - $139,648.35 / Yearly

Benefits

https://careers.niagarawater.com/us/en/benefits

* *Los Angeles County applicants only** Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers, the California Fair Chance Act, and any other applicable local and state laws.

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