Opportunities

Data Scientist - ML Engineer

AAEC0EB0-9F48-4361-A4E3-F440FA4B8F40
LocationIndia Only
D27E7EE2-D0BE-43EA-B61C-C22ADE2FA375$
Compensation$650 - 700 (USD) / week
7AC9DEAC-3F2A-487E-B616-A085E6B4D7FA
Duration26 Weeks
52C90FFB-CC27-4B10-8E63-415ACA6557E4
Hours40 hours / week
90608BD4-776C-4ABD-B264-60B29B9673AC
Working HoursCET(UTC+1)

Required Skills

skills-icon GCP, Python, Machine Learning

Description

Mandatory :

  • 7 years of experience 
  • ML Engineer, GCP, Python
Preferred Skills:  MLOPS, Vertex AI

Job Description:
  • Take offline models from ML scientists and turn them into a real machine learning production system
  • Build and deploy training and serving pipelines for ML models in GCP
  • Identify and evaluate new technologies to improve performance, maintainability, and reliability of existing machine learning systems
  • 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
  • Scope latency issues for multi-cloud deployments (AWS vs GCP) to enable real-time ML serving
  • 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 containers and Kubernetes in cloud computing environments (AWS/GCP)
  •  Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow/Airflow/ Argo, etc.)
  • Exposure to deep learning approaches and modeling frameworks (PyTorch/ Tensorflow/ Keras, etc.)
  • Strong experience in writing SQL queries for production systems (with BigQuery or other data warehouse tools)
  • Experience working as one or more of the following DevOps Engineer, SRE, Platform Engineer, Infrastructure Engineer, Cloud Engineer, and/or Production Engineer.
  • Familiarity and working knowledge with Kubeflow, Seldon Core, Artifact Registry
  • Knowledge of Unit testing in Python, Mocking, Pytest.
  • 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.
  • Nice to have model monitoring and debugging experience in production

Notes

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