MLOps Engineer
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Job Description
MLOps Engineer
Location: Remote
Duration:
Description:
Job Description-
Design data pipelines and engineering infrastructure to support clients' enterprise machine learning systems at scale
Develop and deploy scalable tools and services for clients to handle machine learning training and inference
Collaborate with the data engineers and data scientists on feature development to containerise and build out the deployment pipelines for new modules
Identify and evaluate technologies to improve performance, maintainability, and reliability of clients' machine learning systems
Support model development, with an emphasis on auditability, versioning, and data security
Facilitate the development and deployment of proof-of-concept machine learning systems
Continuously evaluate the latest packages and frameworks in the ML ecosystem
Model & Data Versioning Automated Version Control & tracking of model versions, along with the data used to train it, and some meta-information like training hyperparameters
Automate applications and Infrastructure deployments using IaC technologies (Terraform, CFT, ARM etc.)
Build MLOps pipelines to support development, experimentation, continuous integration, continuous delivery, verification/validation, and monitoring of AI/ML models
Provision and configure secure high-performance computing environments (clusters, storage, API gateways etc) to support hosting of ML/DL models at scale
Lead and drive the deployment, life cycle management and monitoring of Machine Learning (ML) and Deep Learning (DL) models in all stages leading to production
Communicate with clients to build requirements and track progress
Qualifications
5-9 Years of Experience in building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)
Understanding of Python Code
Cloud knowledge (Azure, AWS)
IaC Technologies- Terraform, CFT, ARM etc.
Experience building custom integrations between cloud-based systems using APIs
Experience developing and maintaining ML systems built with open-source tools
Experience developing with containers and Kubernetes in cloud computing environments
Familiarity with one or more data-oriented workflow orchestration frameworks (Kubeflow, Airflow, Argo, etc.)
Ability to translate business needs to technical requirements
Strong understanding of CI/CD tools like Azure DevOps, Github Actions, Jenkins etc.
Proficient in leveraging CI/CD tools to automate testing and deployment
Exposure to Machine Learning methodology and best practices
Exposure to deep learning approaches and modelling frameworks (PyTorch, TensorFlow etc.)
Working Knowledge of different monitoring tools
Strong working understanding of networking, including load balancing and firewall methodologies on Cloud and in General
Excellent written and verbal communication skills with ability to communicate both technical issues to nontechnical and technical audiences
Employment Type
Full Time
