Machine Learning Engineer Jobs in Riverside, CA (NOW HIRING)

Job Summary:
Niagara Bottling is seeking a Sr. Engineer Data Science & Agentic AI to develop and implement advanced data science and AI products focused on machine maintenance. The role includes managing the entire data science lifecycle, optimizing integration across data pipelines, and leading the strategy for machine learning and agentic AI initiatives.
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.
Qualifications:
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
• 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
Company:
Niagara Bottling provides bottled water and bottled sports drinks. Founded in 1963, the company is headquartered in Philadelphia, USA, with a team of 5001-10000 employees. The company is currently Late Stage.