Sr. Engineer Data Science & Agentic AI

Niagara Bottling•Diamond Bar, CA

1d•$111,766 - $159,267

About The Position

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. As a Data Science & Agentic AI Sr. Engineer, you will develop, design, implement, and deliver advanced data science, machine learning, and agentic AI products for all aspects of Machine Maintenance. The role creates novel predictive and prescriptive maintenance solutions, including AI agents and multi-agent workflows that can retrieve knowledge, reason over asset and maintenance data, use approved tools and APIs, and recommend or automate maintenance actions with appropriate human oversight. You will perform data wrangling, exploratory, descriptive, predictive, prescriptive, and agent-enabled analysis and visualization on both a recurring and ad hoc basis in support of the Projects Manager and the Maintenance user base. The Data Science & Agentic AI Sr. Engineer will identify new opportunities for intelligent process automation, Agentic AI, KPIs, visualizations, reports, dashboards, and decision-support products by aligning organizational insight requests with leadership’s strategic objectives.

Requirements

5-7 years - Experience in Python, R, or another programming language
5-7 years - Experience with TensorFlow, PyTorch, scikit-learn, or comparable ML frameworks
3-5 years - Experience in Industrial ML, Automation, Data Science, AI, or related fields
3-5 years - Experience with cloud computing platforms such as AWS, Azure, or GCP
2-4 years - Experience with natural language processing (NLP), LLM applications, prompt engineering, or retrieval-augmented generation (RAG)
2-4 years - Experience designing or deploying Agentic AI solutions, AI agents, RAG applications, LLM-powered workflows, or intelligent automation
2-4 years - Experience with agent orchestration and LLM application frameworks or platforms such as LangChain, LlamaIndex, Microsoft Semantic Kernel, AutoGen, Azure AI Foundry, OpenAI API, or equivalent
3-5 years - Experience with Deep Learning, Computer Vision, Reinforcement Learning, or advanced predictive modeling
2-4 years - Experience with ethical, legal, privacy, security, and responsible AI considerations in machine learning and agentic AI systems
2-4 years - Experience implementing AI guardrails, prompt/agent evaluation, telemetry, human-in-the-loop review, and model or agent monitoring
Experience may include a combination of work experience and education
Proficiency in Azure ML Studio and related tools for model development, deployment, and monitoring.
Proficiency in Agentic AI and LLM application development, including prompt engineering, RAG, vector search/embeddings, function/tool calling, and agent workflow orchestration.
Experience with Agentic AI frameworks or platforms such as LangChain, LlamaIndex, Microsoft Semantic Kernel, AutoGen, CrewAI, Azure AI Foundry, OpenAI API, or equivalent.
Ability to design secure AI agent integrations with APIs, databases, CMMS/EAM platforms, cloud services, and industrial data sources while enforcing least-privilege access and approval gates.
Ability to evaluate and monitor AI agent performance using offline and online evaluations, trace logs, quality metrics, guardrails, human feedback, and incident response processes.
Understanding of Responsible AI, privacy, prompt-injection risks, model/tool misuse, auditability, and governance for autonomous or semi-autonomous AI agents.
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
Scripting and programming skills such as Python, SQL, JavaScript, C++, C#, or API-based integration for analytics, automation, and AI agent tool development
Basic understanding of PLC/SCADA systems such SIEMENS S7, ALLEN BRADLEY, BnR, Edge data Management, etc.
Understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, decision forests, gradient boosting, neural networks, and LLM-based approaches
Able to translate data, model outputs, and AI agent recommendations into actionable decisions for 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
Bachelor's Degree in Computer Science, Data Science, Artificial Intelligence, Industrial/Automation Engineering, or other related fields or equivalent experience

Nice To Haves

7-10 years - Experience in Python, R, or another programming language
7-10 years - Experience with TensorFlow, PyTorch, scikit-learn, or comparable ML frameworks
5-7 years - Experience in Industrial ML, Automation, Data Science, AI, or related fields
5-7 years - Experience with cloud computing platforms such as AWS, Azure, or GCP
3-5 years - Experience with natural language processing (NLP), LLM applications, prompt engineering, or retrieval-augmented generation (RAG)
3-5 years - Experience leading production Agentic AI, LLM, RAG, or multi-agent orchestration initiatives in industrial, manufacturing, maintenance, reliability, or enterprise operations environments
5-7 years - Experience with Deep Learning, Computer Vision, Reinforcement Learning, or advanced predictive modeling
3-5 years - Experience with ethical, legal, privacy, security, and responsible AI considerations in machine learning and agentic AI systems
3-5 years - Experience with AgentOps/LLMOps practices, including monitoring, evaluation, versioning, safety testing, audit trails, and cost/performance optimization
Experience may include a combination of work experience and education
Preferred experience with common data science and AI toolkits, such as R, Weka, Python, NumPy, Matplotlib, Pandas, MATLAB, Azure ML, and LLM/agent development libraries
Master's Degree in Computer Science, Data Science, Artificial Intelligence, Industrial/Automation Engineering, or related field

Responsibilities

Lead the entire data science, machine learning, and agentic AI lifecycle, from problem definition and data collection through model/agent design, deployment, monitoring, governance, and continuous improvement.
Ensure seamless integration and coordination across data pipelines, ML/DL models, LLM applications, agentic workflows, APIs, and business processes, optimizing for safety, scalability, efficiency, and business impact.
Establish robust monitoring mechanisms for deployed models and AI agents, enabling proactive identification of performance, reliability, drift, safety, cost, and governance issues.
Define and execute the overall machine learning, deep learning, and Agentic AI strategy aligned with business goals, maintenance reliability priorities, and enterprise technology standards.
Work closely with stakeholders to identify opportunities for advanced analytics, predictive modeling, AI agents, and intelligent workflow automation that improve maintenance reliability, decision quality, and operational efficiency.
Define and lead the Agentic AI roadmap for predictive maintenance, maintenance knowledge management, work-order triage, troubleshooting, root-cause analysis, and prescriptive reliability workflows.
Design, build, and deploy AI agents and multi-agent workflows using large language models (LLMs), retrieval-augmented generation (RAG), vector search, tool/function calling, workflow orchestration, and secure API integrations.
Integrate agentic workflows with CMMS/EAM, maintenance, asset, IoT, historian, PLC/SCADA, cloud, and enterprise data platforms while maintaining human-in-the-loop controls for higher-risk actions.
Establish AgentOps, LLMOps, and MLOps practices for prompt/version management, agent evaluation, observability, guardrails, traceability, cost monitoring, model drift detection, and continuous improvement.
Implement agentic AI safety, privacy, and security controls, including least-privilege access, data protection, prompt-injection mitigation, approval gates, audit trails, and responsible AI governance.
Drive large-scale data science, ML/DL, and Agentic AI projects that leverage data transformation, machine learning models, LLM applications, and intelligent workflow automation.
Develop first-class predictive maintenance tools, AI agents, and insights for customers by balancing data complexity, coding/visualization platforms, reliability requirements, risk controls, and client demands.
Automate and streamline projects, reports, maintenance workflows, and agent-enabled decision processes to increase efficiency, scalability, and adoption.
Develop alternative procedures, data products, agent tools, and processing methods to optimize data interactions, human-machine collaboration, and new insights.
Contribute to storyboarding activities, developing recommendations for an executive-level audience, and producing leadership-quality deliverables
Manage and review ad hoc automation, AI agent, analytics, and information product support requests.
Work closely with project management teams, IT professionals, reliability engineers, maintenance leaders, and business stakeholders to identify opportunities for AI agents, ML models, and automation to enhance maintenance execution and project management.
Document project requirements, methodologies, architecture decisions, agent workflows, evaluation results, risks, and outcomes.
Prepare technical reports, presentations, and user guides to effectively communicate AI/ML/Agentic AI solutions to stakeholders.
Stay updated with the latest advancements in AI/ML, Agentic AI, LLMs, RAG, vector search, orchestration frameworks, and industrial automation.
Conduct research and experiments to explore new approaches and improve existing models and agents.
Ensure compliance with ethical standards and legal requirements when dealing with sensitive data, privacy, bias, explainability, autonomy, human oversight, and potential misuse of AI/ML models or AI agents.
Share expertise in AI/ML, Agentic AI, responsible automation, and insights with colleagues, stakeholders, and team members.
Conduct training sessions or workshops to facilitate effective utilization of machine learning programs, AI agents, libraries, and governance practices.
Knowledge of data acquisition and data Engineering for manufacturing

Benefits

Regular full-time team members are offered a comprehensive benefits package, while part-time, intern, and seasonal team members are offered a limited benefits package.
Paid Time Off for holidays, sick time, and vacation time
Paid parental and caregiver leaves
Medical, including virtual care options
Dental
Vision
401(k) with company match
Health Savings Account with company match
Flexible Spending Accounts
Expanded mental wellbeing benefits including free counseling sessions for all team members and household family members
Family Building Benefits including enhanced fertility benefits for IVF and fertility preservation plus adoption, surrogacy, and Doula reimbursements
Income protection including Life and AD&D, short and long-term disability, critical illness and an accident plan
Special discount programs including pet plans, pre-paid legal services, identity theft, car rental, airport parking, etc.
Tuition reimbursement, college savings plan and scholarship opportunities
And more!