AI Summary
Shape the future of communications by setting up and enhancing CI/CD for machine learning models, configuring products, and troubleshooting systems. Automate day-to-day MLOps operations and maintain infrastructure documentation. Collaborate with offshore teams and data scientists to integrate ML models into production.
Key Highlights
Set up and enhance CI/CD for machine learning models
Configure products to meet functional requirements
Troubleshoot and remediate machine learning systems
Technical Skills Required
Benefits & Perks
Competitive salary and bonus
Unifonic share scheme
30 holiday days after first anniversary
Flexible working arrangements
Up to 25 days per year working from anywhere
Paid leave for new parents
LinkedIn learning license
Job Description
Help us shape the future of communications by:
- Setting up and enhancing CI (Continuous Integration) and CD (Continuous Deployment) for machine learning models, including data pipelines, model training, inference and deployment.
- Configuring the company's products to meet the functional requirements, including the configuration of functional areas and technical areas (e.g. integration interfaces, integration maps, web services, transport protocols, etc...).
- Troubleshooting and remediating of machine learning Systems, diagnose and resolve issues impacting the integration and operations of the infrastructure and systems.
- Ensuring high availability of Machine learning products and platforms of the company’s [24x7x365], including but not limited to messaging, voice, and e-mail gateways, in addition to assessing emergencies, providing analysis, and recommending solutions.
- Writing and deploying scripts in different environments to automate day-to-day MLOps operations, including data ingestion, model training, model validation, and model serving.
- Maintaining servers’ configuration, monitoring jobs, and infrastructure documentation across the cloud environment (AWS, Azure, etc). specifically tailored for machine learning workloads and GPU utilization.
- Working extensively with offshore teams, data scientists, and machine learning engineers on a daily basis to integrate ML models into production and ensure operational excellence.
Job requirements
What you will bring:
- Hands-on 5+ years of recent technical experience in a Professional Services/Enterprise DevOps role or MLOps.
- Strong technical skills and proficiency in multiple functional areas are required; such as infrastructure as Code (CloudFormation/Terraform), containerize orchestration (Docker, Kubernetes), configuration management (Ansible), programming language (Python), especially for ML-related tasks, logging system (Elastic Stack), CI/CD (DroneCI, Argo CD) , ML-specific monitoring tools, and network protocols and standards.
- Machine Learning Frameworks: Familiarity with popular ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
- MLOps Tools and Platforms: Experience with tools like MLflow or Kubeflow.
- Data Versioning Tools: Understanding of tools like DVC (Data Version Control).
- The ability to spend substantial time interfacing with the offshore teams on a daily basis.
- Fluent in English with excellent writing/editing and verbal communication skills.
- Strong sense of ownership, critical thinking, and ability to drive DevOps and MLOps mindset initiatives across teams for agile release.
- Analytical thinking and being accountable for the results of made decisions.
- Bachelor's degree in Computer Engineering, Software Engineering, or other IT-related fields.
As a Unifone you will receive a range of benefits:
- Competitive salary and bonus.
- Unifonic share scheme (we are all owners!).
- 30 holiday days after your first anniversary.
- Flexible working arrangements.
- Spend up to 25 days per year working from anywhere in the world!
- Paid leave for new parents.
- LinkedIn learning license.