ML Engineer
Los Angeles, United States
iLink Digital
About
The Company:
What makes iLink
Systems' offerings unique is the fact that we use
pre-created frameworks, designed to accelerate software
development and implementation of business processes for our
clients. iLink has over 60 frameworks (solution accelerators),
both industry-specific and horizontal, that can be easily
customized and enhanced to meet your current business
challenges.
Requirements
We are seeking a talented and experienced Machine Learning Operations (ML Ops) Engineer with 2-4 years of hands-on experience in deploying and managing machine learning models in a Microsoft Azure environment. As an ML Ops Engineer at iLink, you will play a crucial role in ensuring the scalability, reliability, and efficiency of our machine learning workflows and production systems.Responsibilities:
- Model Deployment and Management: Deploy machine learning models in Microsoft Azure using Azure Machine Learning Services and other relevant tools, ensuring smooth integration with our production systems.
- Automation and Orchestration: Develop and maintain automated pipelines and workflows for model training, deployment, and monitoring using tools such as Azure DevOps or other CI/CD platforms.
- Infrastructure Management: Provision, configure, and optimize Azure infrastructure components such as virtual machines, Kubernetes clusters, and data storage to support machine learning workloads.
- Monitoring and Logging: Implement robust monitoring and logging solutions to track the performance and health of deployed models and infrastructure. Set up alerts for proactive issue identification and resolution.
- Scalability and Performance: Collaborate with data scientists and engineers to optimize the performance of machine learning models, ensuring scalability to handle increasing data volumes and user loads.
- Security and Compliance: Enforce security best practices for machine learning models and data, ensuring compliance with industry standards and regulations.
- Version Control: Implement version control for machine learning models and associated code to track changes and facilitate collaboration among data science and engineering teams.
- Documentation: Maintain clear and up-to-date documentation for ML Ops processes, configurations, and best practices.
- Troubleshooting: Diagnose and resolve issues related to model deployment, infrastructure, and data pipelines on time.
- Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, and data engineers, to ensure seamless integration of machine learning solutions into production systems.
Qualifications:
- Bachelor's degree in computer science, engineering, or a related field. Master's degree is a plus.
- 2-4 years of experience in ML Ops or related roles, with a strong background in deploying and managing machine learning models in a production environment.
- Proficiency in Microsoft Azure, including Azure Machine Learning Services, Azure DevOps, and Azure Kubernetes Service (AKS).
- Experience with containerization technologies such as Docker and container orchestration using Kubernetes.
- Strong scripting and programming skills in languages like Python, and familiarity with machine learning libraries and frameworks.
- Knowledge of CI/CD pipelines and version control systems (e.g., Git).
- Excellent problem-solving and troubleshooting skills.
- Strong communication and collaboration skills to work effectively with cross-functional teams.
- Azure certifications (e.g., Azure AI Engineer Associate, Azure DevOps Engineer Expert) are a plus.
Benefits
-
Competitive
salaries
-
Medical,
Dental, Vision Insurance
-
Disability,
Life & AD&D Insurance
-
401K
With Generous Company Match
-
Paid
Vacation and Personal Leave
-
Pre-Paid
Commute Options
-
Employee
Referral Bonuses
-
Performance
Based Bonuses
-
Flexible
Work Options & Fun Culture
-
Continuing
Education Reimbursements
-
In-House
Technology Training
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Azure CI/CD Computer Science Data management Data pipelines DevOps Docker Engineering Git Kubernetes Machine Learning ML models Model deployment Model training Pipelines Python Security
Perks/benefits: 401(k) matching Career development Flex vacation Health care Insurance Medical leave Salary bonus
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