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A unique technology group with a very human purpose, on a journey, to invent visionary artificial intelligence for a better everyday. |
Looking for an ambitious, passionate and creative-minded Engineer MLOps, to support the development of MLOps at Inception, driving and enabling automated machine learning model deployments and managing the full lifecycle of our ML projects.
The opportunity
To play a key supporting role in shaping the overall MLOps strategy & capabilities at Inception.
Inception is the UAE’s national-scale enabler in AI Research and Development. Partnering with Microsoft\'s AI SaaS, we offer domain-specific Agentic AI Orchestrator platforms utilizing reasoning agents for precise and cost-effective services. Our focus includes AI incubation, IP creation, applied AI R&D, and AI investment products. By creating models tailored to specific domains and languages, we ensure superior accuracy and efficiency. Collaborating with top universities and industry giants to drive significant advancements in AI technology within the region.
Responsibilities
Your key responsibilities
Reporting to the Lead Engineer - MLOps you will be responsible for building the foundation of our MLOps capabilities, working closely with Data Scientists, Data Engineers and multiple Technology Service departments to manage the end-to-end ML lifecycle including automation of machine learning workflows, code deployments, testing, and data validation processes.
The candidate should be well-versed in using tools like Jenkins, GitLab CI, Azure DevOps, or equivalent, and should be able to integrate these tools with machine learning platforms and data repositories.
Design, implement, and manage CI/CD pipelines tailored for machine learning workflows.
Ensure that machine learning models are properly versioned and deployed into production, staging, or testing environments automatically.
Collaborate with data scientists and software engineers to optimize the automation process for training, validating, and deploying machine learning models.
Continuously monitor the performance and reliability of CI/CD pipelines and make adjustments as necessary.
Set up environments and fully implement scalable machine learning operations.
Continuously monitor, optimize, debug and automate MLOps pipelines for increased quality and efficiency—including pipeline level, module level, and system-level inspection.
Develop & maintain the infrastructure & tools that facilitate the deployment and monitoring of our ML algorithms in production.
Keep abreast of the latest technology trends to drive standard methodologies and stay ahead of the curve.
Document and track all systems, pipelines and best practices.
The opportunity
To play a key supporting role in shaping the overall MLOps strategy & capabilities at Inception.
Inception is the UAE’s national-scale enabler in AI Research and Development. Partnering with Microsoft\'s AI SaaS, we offer domain-specific Agentic AI Orchestrator platforms utilizing reasoning agents for precise and cost-effective services. Our focus includes AI incubation, IP creation, applied AI R&D, and AI investment products. By creating models tailored to specific domains and languages, we ensure superior accuracy and efficiency. Collaborating with top universities and industry giants to drive significant advancements in AI technology within the region.
Responsibilities
Your key responsibilities
Reporting to the Lead Engineer - MLOps you will be responsible for building the foundation of our MLOps capabilities, working closely with Data Scientists, Data Engineers and multiple Technology Service departments to manage the end-to-end ML lifecycle including automation of machine learning workflows, code deployments, testing, and data validation processes.
The candidate should be well-versed in using tools like Jenkins, GitLab CI, Azure DevOps, or equivalent, and should be able to integrate these tools with machine learning platforms and data repositories.
Design, implement, and manage CI/CD pipelines tailored for machine learning workflows.
Ensure that machine learning models are properly versioned and deployed into production, staging, or testing environments automatically.
Collaborate with data scientists and software engineers to optimize the automation process for training, validating, and deploying machine learning models.
Continuously monitor the performance and reliability of CI/CD pipelines and make adjustments as necessary.
Set up environments and fully implement scalable machine learning operations.
Continuously monitor, optimize, debug and automate MLOps pipelines for increased quality and efficiency—including pipeline level, module level, and system-level inspection.
Develop & maintain the infrastructure & tools that facilitate the deployment and monitoring of our ML algorithms in production.
Keep abreast of the latest technology trends to drive standard methodologies and stay ahead of the curve.
Document and track all systems, pipelines and best practices.
