Fluor is seeking a Senior Machine Learning Engineer to design, develop, and deploy scalable ML solutions that support complex EPC projects across energy, chemicals, infrastructure, and advanced manufacturing domains. This role focuses on translating business and engineering problems into data driven models that improve safety, cost predictability, schedule performance, and asset reliability. The successful candidate will work closely with data architects, domain engineers, and global stakeholders. This is a hands-on technical role with opportunities to influence enterprise level analytics platforms.

Key Responsibilities

Design, develop, and deploy machine learning models for predictive analytics, optimization, and decision support across EPC project lifecycles
Translate engineering, construction, and operations use cases into ML solutions (e.g., cost forecasting, schedule risk, quality defects, equipment reliability)
Build end-to-end ML pipelines including data ingestion, feature engineering, model training, validation, and monitoring
Collaborate with data architects to ensure scalable, secure, and compliant data and ML architectures
Apply statistical analysis and advanced ML techniques (supervised, unsupervised, time series, NLP where applicable)
Optimize model performance and reliability for production environments
Partner with business stakeholders to communicate insights, assumptions, and model limitations clearly
Ensure adherence to data governance, cybersecurity, and privacy standards
Support deployment using CI/CD and MLOps best practices
Mentor junior engineers and contribute to technical standards and reusable frameworks

Basic Job Requirements

Bachelor s degree in Computer Science, Engineering, Mathematics, or a related discipline
Minimum 5 years of progressive experience in machine learning or applied data science roles
Strong proficiency in Python / R , ML libraries (scikit learn, TensorFlow, PyTorch, XGBoost, etc.)
Solid understanding of statistics, probability, and model evaluation techniques
Experience working with structured and unstructured data at scale
Proven ability to deploy ML models into production environments
Strong problem solving, communication, and stakeholder engagement skills
Bachelor s degree required; Master s degree preferred
Relevant certifications in ML, AI, or Cloud (Azure/AWS/GCP) are a plus

Other Job Requirements

Preferred Qualifications

Master s degree in Data Science, AI, or a related field
Experience in EPC, engineering, construction, manufacturing, or asset intensive industries
Exposure to time series forecasting, anomaly detection, or optimization models
Experience with cloud platforms (Azure preferred)
Familiarity with MLOps tools and practices
Experience working in global, matrixed organizations

ML Engineer (Classical ML)

Job Description

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