ALOIS Solutions | Machine learning Operations Architect | nashville, tn

3 days ago

nashville, United States ALOIS Solutions Full time

Job title: MLOPS Architect

Duration: Full-time/Permanent role

Location: Nashville, TN (Remote)


Note : CPG (Consumer Packaging Goods) and Supply Chain


Overview:

Notes:

1) A Minimum of 7-10 years of CPG Hands-on experience is required (Recent CPG experience).

2) Experience in Strategic Revenue Management.

3) Ml ops Architect - Azure experience is required

4) Data Science - How to build pricing models, etc.

5) ML Ops - how to build the code repository, specifically for SRM

Job Description:

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.

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