Sr. Engineer Data Science & Agentic AI

Sr. Engineer Data Science & Agentic AI

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.

Full Spectrum Data Science and Agentic AI Management

• 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.
• ML/DL and Agentic AI Strategy Development:
• 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.
• Agentic AI Strategy and Execution:
• 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.
• ML/DL and Agentic AI Implementation and Execution:
• 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.
• Collaboration: 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.
• Documentation: 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.
• Research and Innovation: 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.
• Ethical, Legal, and Responsible AI Considerations: 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.
• Training and Knowledge Sharing: 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
• 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.

Data Science & Agentic AI Manager 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:

• 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

Preferred:

• 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
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