Role: Senior Consultant - AI/ML
Location - Houston, TX (Hybrid, Needs to be able to travel to Houston every other week)
Role Summary
As a Senior Consultant (L6), 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 mentor L4 (Associate) and L5 (Staff) consultants on the team, providing technical guidance and reviewing their deliverables. 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 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
