Architect (Machine Learning) - Straive

We are seeking a highly skilled Lead Architect in ML Ops with over 7 years of experience, specifically within the Consumer Packaged Goods (CPG) domain. The ideal candidate will have a strong focus on revenue management, and will be responsible for building and optimizing SRM-specific pipelines. This role demands a deep understanding of machine learning lifecycle management and the ability to design and execute robust MLOps solutions.

Key Responsibilities:

Collaboration: Partner with data scientists and ML engineers throughout the entire ML lifecycle, from model development to production deployment.

MLOps Pipeline Design: Design and implement MLOps pipelines utilizing tools and frameworks such as TensorFlow Serving, Kubeflow, MLflow, or similar solutions.

Data Engineering Infrastructure: Develop and implement data pipelines and engineering infrastructure to support enterprise-scale machine learning systems, including tasks like data ingestion, preprocessing, transformation, feature engineering, and model training.

Cloud Solutions: Design and implement cloud-based MLOps solutions on platforms like Azure ML, Azure Databricks, AWS SageMaker, or Google Cloud AI Platform.

Azure Expertise: Demonstrated hands-on experience with Azure cloud services, including Azure ML, Azure DevOps, AKS, Azure Container Registry (ACR), Azure Application Insights, and Azure Log Analytics.

Containerization: Experience with containerization technologies like Docker and Kubernetes is advantageous.

Model Deployment: Deploy and maintain various types of machine learning models in production, particularly in text/NLP and generative AI applications.

CI/CD Pipeline Development: Build CI/CD pipelines using tools such as GitLab CI, GitHub Actions, Airflow, or similar solutions to automate the ML lifecycle.

Model Review & Optimization: Conduct data science model reviews, focusing on code refactoring, optimization, containerization, deployment, versioning, and monitoring model quality.

Support Model Development: Facilitate data model development with a focus on auditability, versioning, and data security, including practices like lineage tracking, model explainability, and bias detection.

Mentorship: Mentor junior MLOps engineers and collaborate with consulting, data engineering, and development teams.

Qualifications:

Experience: Minimum of 7 years of work experience, with at least 3 years focused on MLOps.

Domain Expertise: Strong expertise in Generative AI, advanced NLP, computer vision, and ML techniques.

Production Solutions: Proven experience in designing, developing, and deploying production-grade AI solutions.

Communication Skills: Excellent communication and collaboration skills, with the ability to work independently and as part of a team.

Analytical Skills: Strong problem-solving and analytical abilities.

Continuous Learning: Stay current with the latest advancements in MLOps technologies and actively evaluate new tools and techniques to enhance the performance, maintainability, and reliability of machine learning systems.