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