Machine Learning Engineer II $100k - $145k in Diamond Bar at Niagara Bottling
Niagara Bottling

Machine Learning Engineer II

Niagara Bottling • Diamond Bar, CA, US
Full-time $100k - $145k Posted about 1 month ago

Responsibilities

  • check_circle Advanced ML Modeling & Algorithmic
  • check_circle Supervised & Unsupervised Learning: Build robust classifiers for fault diagnosis and regression models for Remaining Useful Life (RUL) estimation. Expertly handle highly imbalanced datasets where failure labels are rare.
  • check_circle Agentic AI & Prescriptive Systems: Develop multi-agent workflows that reason over asset health data, parse digital manuals via RAG (Retrieval-Augmented Generation), interact with operational APIs, and generate automated outputs.
  • check_circle Utilizing XGBoost, Random Forests, LSTMs, and Autoencoders—to process sensor streams and PLC data for predictive maintenance and real-time anomaly detection. leverage techniques like Isolation Forests, One-Class SVMs, Dynamic Time Warping, and PCA to build scalable models that monitor asset health, classify process quality, and drive automated decision-making.
  • check_circle Production-Grade MLOps & Infrastructure
  • check_circle Robust Data Engineering: Standardize, clean, and enrich raw, unstructured, or missing sensor telemetry and PLC tag data.
  • check_circle Scalable ML Pipelines: Build and maintain scalable, reproducible training and inference pipelines (using MLflow, Kubeflow, or Azure Machine Learning).
  • check_circle Edge & Cloud Deployment: Deploy models across hybrid environments, optimizing for cloud (Azure) as well as low-latency.
  • check_circle Distributed Compute Tuning: Optimize model training and throughput, leveraging GPU-accelerated training and efficient serialization for massive datasets.
  • check_circle Systems Integration & Cross-Functional Impact
  • check_circle High-Fidelity Code: Deliver highly optimized, production-grade, modular software in Python and C++ accompanied by strict unit testing, and clean documentation.
  • check_circle Technical Communication: Bridge the gap between data science and physical operations. Clearly articulate complex ML mechanics, decision boundaries, and model limitations to plant managers, IT directors, and executive leadership.
  • check_circle Frameworks & Libraries: Deep expertise in PyTorch or TensorFlow, alongside standard data science libraries (Scikit-Learn, NumPy, Pandas, SciPy).
  • check_circle Production Programming: Exceptional software development skills in Python (writing optimized, vectorized code) ,Java Script, C/C++ & R
  • check_circle Modern MLOps & Cloud: Hands-on experience with containerization (Docker/Kubernetes), distributed processing (PySpark/Databricks), and cloud architectures, ideally Microsoft Azure.
  • check_circle Data Handling: Mastery of SQL, NoSQL, and time-series databases (e.g., InfluxDB, TimescaleDB) containing millions of streaming data points.
  • check_circle This position is estimated to travel 10-30%
  • check_circle 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.
  • check_circle Lead Like an Owner
  • check_circle Manages a safe working environment, accurately documents safety-related training, and effectively communicates safety incidents
  • check_circle Provides strategic input and oversight to departmental projects
  • check_circle Makes data-driven decisions and develops sustainable solutions
  • check_circle Skilled in reducing costs and managing timelines while prioritizing long-run impact over short-term wins
  • check_circle Makes decisions by putting overall company success first before department/individual success
  • check_circle Leads/facilitates discussions to get positive outcomes for the customer
  • check_circle Makes strategic decisions that prioritize the needs of the customer over departmental/individual goals
  • check_circle InnovACT
  • check_circle Continuously evaluates existing programs and processes, and develops new initiatives to increase efficiency and reduce waste
  • check_circle Creates, monitors, and responds to departmental performance metrics to drive continuous improvement
  • check_circle Champions responsible adoption of Agentic AI and intelligent automation to improve reliability, speed, decision quality, and waste reduction while maintaining safety and governance.
  • check_circle Communicates a clear vision, organizes resources effectively, and adjusts the strategy as needed when managing change
  • check_circle Find a Way
  • check_circle Demonstrates ability to think analytically and synthesize complex information
  • check_circle Effectively delegates technical tasks to subordinates
  • check_circle Works effectively with departments, vendors, and customers to achieve organizational success
  • check_circle Identifies opportunities for collaboration in strategic ways
  • check_circle Empowered to be Great
  • check_circle Makes hiring decisions primarily based on culture fit and attitude, and secondarily based on technical expertise
  • check_circle Engages in long-term talent planning
  • check_circle Provides opportunities for the development of all direct reports
  • check_circle Understands, identifies, and addresses conflict within own team and between teams
  • check_circle Required:
  • check_circle Education: Bachelor’s degree in Computer Science, Data Science, Electrical/Mechanical Engineering, Mathematics, or related quantitative field (Master’s or PhD with an ML focus is highly preferred).
  • check_circle Strong proficiency in Python and modern machine learning frameworks such as PyTorch and/or TensorFlow
  • check_circle Experience working across multiple modalities, with expertise in one or more of: Natural Language Processing: LLMs, text classification, information extraction, retrieval systems, agentic applications, or related areas.
  • check_circle Natural Language Processing: LLMs, text classification, information extraction, retrieval systems, agentic applications, or related areas.
  • check_circle Experience training, fine-tuning, evaluating, and deploying machine learning models in production environments.
  • check_circle Experience designing evaluation methodologies, benchmarking systems, and model performance metrics.
  • check_circle Experience with MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.)
  • check_circle Experience with cloud platforms such as Google Cloud Platform (preferred), AWS, or Azure, including ML infrastructure, workflow orchestration, storage, and database services.

Preferred qualifications

  • Masters or PhD degrees are preferred.
  • 5-7 years - Experience in Python, R, or another programming language
  • 5-7 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
  • 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.
  • 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
  • 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
  • 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
  • Minimum Required: Bachelor's Degree in Computer Science, Data Science, Artificial Intelligence, Industrial/Automation Engineering, or other related fields or equivalent experience
  • Bachelor's Degree in Computer Science, Data Science, Artificial Intelligence, Industrial/Automation Engineering, or other related fields or equivalent experience
  • Preferred: Master's Degree or PhD in Computer Science, Data Science, Artificial Intelligence, Industrial/Automation Engineering, or related field
  • Master's Degree or PhD in Computer Science, Data Science, Artificial Intelligence, Industrial/Automation Engineering, or related field
  • Required: N/A
  • Preferred: N/A

Benefits

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

Tags & Focus Areas

Fulltime Machine Learning Ai

About Niagara Bottling

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