ML Ops Engineer

6 days ago

Noida, Uttar Pradesh, India Topcoder Full time ₹ 12,00,000 - ₹ 36,00,000 per year
Mandatory Skills:

ML Model Deployment, GKE(Google Kubernet Engine)/AWS/Azure, MLOps(Devops)

Preferred Skills:

KubeFlow, Vetex AI,  GCP/ Any Cloud, VM(Virtual Machine).

Job Description / Roles & Responsibilities
  • Build and deploy training and serving pipelines for ML models in GCP
  • Take offline ML models developed by other ML scientists and turn them into a machine learning production system (both offline batch and realtime inference)
  • Optimize inference pipeline latency for cloud deployments to enable real-time ML serving
  • Work with upstream and downstream engineering teams to define the data contract for serving in platforms such as KubeFlow and Vertex AI
  • Work as a liaison with ML Science and Data Engineering team to understand the data schema and convey changes and updates to the respective teams
  • Identify and evaluate new technologies to improve performance, maintainability, and reliability of existing machine learning systems
  • Enable dataset permissions, onboard pip packages to enterprise ML platform registry for Security approval, creating custom Docker images from base Python images
Qualifications:
  • Experience developing with Docker containers and Kubernetes in cloud computing environments (AWS/GCP)
  • Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, MLFlow, Argo, etc.)
  • Familiarity and working knowledge with Kubeflow, Seldon Core, Artifact Registry or other equivalents.
  • Strong SQL experience for writing model pipelines in production systems (e.g., BigQuery)
  • Exposure to Deep Learning frameworks (PyTorch, Tensorflow, Keras, etc.)
  • Knowledge of model serialization and deserialization for Pytorch, Scikitlearn and Tensorflow
  • Knowledge of Git and Github. Gitops, Bazel and CI/CD deployments with Jenkins
  • Understanding of MLOps, Model development lifecycle with knowledge of Training and Deployment pipelines for Machine Learning solutions
  • Experience working as one or more of related MLOps roles - DevOps Engineer, SRE, Platform Engineer, Infrastructure Engineer, Cloud Engineer, and/or Production Engineer.
  • Knowledge of Unit testing in Python, Mocking, and Pytest.
  • Nice to have model monitoring and debugging experience in production
Project Description:

​​​​​​​
  • As a Machine Learning Engineer within the Applied Machine Learning (AML) org, you will be working closely with the other ML colleagues to deploy their ML models into GCP as a batch pipeline or realtime micro-service made available to millions of Best Buy ecommerce customers. Specifically, we will be deploying cutting-edge Recommender Systems (e.g., Two Tower Models) to recommend the right products at the right time to the right customers in the online ecommerce platforms in Web and App. These RecSys incorporate the latest advances in Deep Learning such as Embeddings to make it easier for the customers to shop online. You will be partnering with Recommendations Product & Engineering teams regularly to figure out how to integrate the ML solution into their tech stack.

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