ML Engineer

North Carolina's Electric Cooperatives•Raleigh, NC

About The Position

The ML engineer will be responsible for managing the full Machine Learning lifecycle—from data processing and model development to deployment, monitoring and training —within a cloud-native, collaborative environment. This position will also be accountable for making recommendations to strengthen system controls, increasing efficiency using automated processes, and documenting technical requirements and specifications. This role will also be responsible for making system and application recommendations based upon industry best practices, technical viability, and operational standards. As a ML engineer, this individual will be expected to mentor junior staff and provide guidance in solution design, as well as to contribute to the platform design and scalability.

Requirements

Bachelor's degree in Computer Science, Computer Information Systems, Computer Engineering, Math, or related technical degree from an accredited institution, and/or equivalent experience.
Five to ten years of demonstrated work experience with a minimum of three years of experience in developing, optimizing deploying machine learning models in production environment.
Databricks platform experience required
Awareness of Lakehouse architecture, cluster management and Spark architecture
Experience with MLflow, AutoML, Feature store
Familiarity of CI/CD, git repos and version control process required
Experience with Python, SQL and ML frameworks such as scikit, TensorFlow, XGBoost, PyTorch or similar
Experience with model training, evaluation, hyperparameter tuning and optimization
Understanding of MLOps best practices
Ability to do data preparation, feature engineering, and exploratory data analysis
Excellent listening, interpersonal, written, and oral communication skills
Ability to work independently with minimal supervision and excel at meeting deadlines
Familiarity with the Agile methodology
Ability to work and communicate effectively with business stakeholders and technical staff
Ability to translate business needs into a deliverable solution

Nice To Haves

Experience in the public utility industry or energy sector is a plus.
Familiarity with Large Language Models (LLMs), Generative AI solutions is preferred
Any relevant certification in Databricks and/or ML is a plus
Azure or other cloud platform experience is relevant but is optional
Experience leveraging Cloud services for scalable solutions is preferred

Responsibilities

Design, develop, and deploy scalable machine learning models
Optimize model performance and inference speed
Process, transform, and analyze large-scale datasets
Optimize workloads for cost and performance efficiency
Ensure adherence to data governance policies, security standards and any regulatory compliance
Collaborate with cross-functional teams such as data engineers, data scientists, analysts, business and other stakeholders
Document ML solutions, workflows and best practices
Conduct code reviews and mentor peers and junior staff in ML engineering
Proactively review, streamline, and suggest improvements to our current processes and documentation
Stay current with industry trends and platform updates
Take a leading role in defining the processes, standards, and roadmap for machine learning solutions to drive innovation