Platform Engineer

Platform Engineer

Walmart Aspectiva is seeking a Platform Engineer for DevOps / MlOps to develop and maintain cloud services and CI/CD pipelines, facilitating efficient machine learning model development.In this role, you will collaborate closely with the data science team and have the freedom to design and implement optimization strategies. You will also manage and maintain a scalable, robust infrastructure specifically for ML projects, supporting ML engineers throughout the MLOps cycle.Responsibilities

As a Platform Engineer, you will be pivotal in enabling our ML engineers to efficiently develop and deploy ML projects. You will work in a big data production environment (millions of events and customers). Your role will involve leveraging various technologies in container orchestration, cloud services, and CI/CD pipelines for data collection and monitoring in production.

• Build and maintain a scalable infrastructure: Develop a robust infrastructure to support the execution of ML projects, focusing on delivering results and user value.
• Design and execute GPU optimization strategies: Ensure efficient use of GPU resources.
• Design and build inference platforms: Create platforms that are reliable and high performing.
• Provision and monitor infrastructure resources: Ensure the availability and performance of necessary resources.
• Build and maintain ML workflows and pipelines: Develop and manage workflows and pipelines to streamline ML processes.
• Deploy and maintain monitoring services: Implement and sustain observability services to monitor system performance.

Qualifications:

• 4+ years of experience in developing production-grade products.
• Proven experience with creating and maintaining CI/CD pipelines.
• Proven experience in building core infrastructure for development,
• inference workloads, ML workflows, and versioning.
• Proficient in implementing AI/ML algorithms, refining and improving models, and integrating them into production services.
• Experienced in building and scaling machine learning infrastructure.
• Adept at running and scaling inference clusters.
• Strong problem-solving skills and ability to thrive in a dynamic environment.
• Excellent written and verbal communication skills.

Advantages:

• Experience in accelerating training and parallel computation using tools like Dask, Spark, and frameworks like ONNX or TensorRT.
• Proficient in serving framework models such as Triton or Ray.
• Hands-on experience with containerization and orchestration tools like Docker and Kubernetes.
• Knowledgeable in machine learning and deep learning methods.
• Familiarity with monitoring and logging tools such as Prometheus and Grafana.