Senior Consultant - AI/ML

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