Architect (Machine Learning)

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.