Machine Learning Platform Architect

TEKsystems•Raleigh, NC

1d•$50 - $65•Hybrid

About The Position

We are seeking a Senior MLOps Engineer to design, build, and scale enterprise-grade machine learning platforms that support the full AI/ML lifecycle. This role requires a hands-on technical leader who can architect robust MLOps solutions while partnering with data scientists, software engineers, and business stakeholders to bring machine learning models into production. The ideal candidate has extensive experience building end-to-end MLOps ecosystems, implementing cloud-native infrastructure, and creating scalable environments that accelerate AI development and deployment.

Requirements

8+ years of software engineering, machine learning engineering, data engineering, or related experience.
4+ years of experience designing and implementing end-to-end MLOps platforms.
Advanced proficiency in Python.
Hands-on experience with PyTorch, TensorFlow, scikit-learn, or similar ML frameworks.
Deep expertise in at least one major cloud platform (AWS, Azure, or GCP) and associated machine learning services.
Strong experience with Kubernetes, Docker, and Terraform.
Experience building and supporting distributed data processing and machine learning pipelines.
Excellent communication, collaboration, and stakeholder management skills.
Demonstrated ability to work independently and lead technical initiatives.

Nice To Haves

Experience supporting large-scale production AI/ML environments.
Background implementing governance, security, and compliance standards for ML platforms.
Experience mentoring engineers and driving technical best practices.
Familiarity with feature stores, model registries, and ML observability tools.
Experience optimizing infrastructure for performance, scalability, and cost efficiency.

Responsibilities

Design and implement end-to-end MLOps platforms supporting data ingestion, feature engineering, model training, model registry, deployment, and monitoring.
Develop and maintain scalable machine learning infrastructure in cloud environments.
Build and optimize CI/CD pipelines for ML workloads and model deployment.
Deploy and manage Kubernetes-based environments, including GPU-enabled clusters.
Implement Infrastructure as Code (IaC) standards using Terraform.
Partner with data science teams to operationalize machine learning solutions.
Establish monitoring, observability, governance, and model performance tracking.
Create scalable and reliable distributed data processing pipelines.
Drive platform standardization, automation, and best practices across AI initiatives.
Communicate technical solutions and recommendations to both technical and non-technical stakeholders.