Lead AI/ML Engineer

Role Summary

As a Lead AI/ML Engineer you are an experienced individual contributor who independently drives one or more AI/ML workstreams end-to-end.

You own delivery outcomes for your assigned workstream, make key technical decisions, and interface directly with customer technical leads and stakeholders.

You bring deep expertise in ML Ops and time-series forecasting to architect and implement scalable solutions on AWS.

Key Responsibilities

• Design, develop, and deploy machine learning models for latent capacity prediction across pipeline systems (weather, gas turbine HP, compressor flow, line pack, equipment performance)
• Build and maintain ML Ops infrastructure on AWS SageMaker including model registry, versioning, CI/CD pipelines, and multi-environment endpoints (Dev, Pre-Prod, Prod)
• Conduct exploratory data analysis and feature engineering for time-series forecasting of pipeline operational data (SCADA, performance curves, hydraulic models)
• Develop ensemble model strategies combining LSTM, Prophet, and XGBoost for improved prediction accuracy of latent capacity
• Build automated model deployment workflows, training/evaluation pipelines, and retraining frameworks
• Design real-time and batch inference architectures with API Gateway integration and model monitoring
• Collaborate with data engineering team on feature stores and data pipeline integration from Bronze/Silver/Gold data lakehouse layers
• Support hydraulic model integration with Gregg Engineering NextGen software for automated scenario generation
• Independently own delivery of assigned ML workstream, driving technical decisions and ensuring quality
• Mentor L4/L5 team members on ML best practices, code reviews, and architectural patterns
• Interface directly with customer technical leads to align on requirements, review progress, and resolve technical blockers

Required Skills & Qualifications

• Strong experience with AWS SageMaker, including SageMaker Pipelines, Model Registry, and Feature Store
• Proficiency in time-series forecasting (LSTM, Prophet, XGBoost, ensemble methods)
• Experience with ML Ops practices: CI/CD for ML, model monitoring, automated retraining
• Python (NumPy, Pandas, scikit-learn, TensorFlow/PyTorch)
• Experience with real-time and batch inference architectures
• Knowledge of data lakehouse architectures (S3, Glue, Redshift)
• Understanding of industrial/operational data (SCADA, IoT sensors) is a plus
• Experience in Energy & Utilities domain preferred
• Demonstrated ability to independently lead technical workstreams and make architectural decisions
• Experience mentoring junior engineers or consultants
• Strong communication skills for customer-facing interactions