Machine Learning Operations (MLOps) Architect (GCP)
Description For Machine Learning Operations (MLOps) Architect (GCP)
Rackspace, a leading public cloud integrator, is seeking a Machine Learning Operations (MLOps) Architect to join their team in a hybrid work environment. This role combines deep technical expertise in machine learning engineering with infrastructure development, focusing on building and optimizing scalable ML platforms and inference systems.
The position requires an experienced professional with 5+ years of industry experience who can architect and implement sophisticated ML solutions using Google Cloud Platform (GCP) services. You'll be working with cutting-edge technologies, including large language models (LLMs), and will be responsible for developing end-to-end MLOps solutions that drive business value.
As an MLOps Architect, you'll lead the design and implementation of ML platforms, develop CI/CD workflows, and ensure robust model deployment and monitoring systems. You'll work closely with cross-functional teams, providing technical leadership while building scalable, cost-effective solutions. The role demands expertise in deep learning frameworks, cloud services, and MLOps best practices.
This is an excellent opportunity for someone passionate about machine learning infrastructure who wants to work with a company that values professional growth and tackles complex business challenges. You'll be at the forefront of implementing innovative ML solutions while working with a collaborative team in a flexible hybrid work environment. The position offers the chance to work with cutting-edge technology while making a significant impact on customer success through ML implementation.
Responsibilities For Machine Learning Operations (MLOps) Architect (GCP)
- Architect and optimize ML Platforms to support machine learning and deep learning models
- Lead end-to-end development and operation of high-performance inference systems
- Develop CI/CD workflows for ML models and data pipelines
- Automate model training, validation, and deployment
- Monitor and maintain ML models in production
- Ensure reproducibility and traceability of experiments
- Manage model versioning and rollbacks
- Collaborate with data scientists and software engineers
- Optimize model inference infrastructure
- Implement data and model governance policies
- Stay current with evolving GCP MLOps practices
Requirements For Machine Learning Operations (MLOps) Architect (GCP)
- Bachelor's degree in computer science, Information Technology, or related field
- 5+ years of relevant industry experience
- Experience with deep learning frameworks (TensorFlow, PyTorch, Hugging Face, LangChain)
- Experience with Google Cloud Platform (GCP) and Vertex AI
- Knowledge of machine learning algorithms, NLP, and statistical modeling
- Strong problem-solving skills and critical thinking
- Excellent written and verbal communication skills
- Experience in building and scaling agentic AI systems
- Understanding of large language model (LLM) architectures
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