Creating a robust machine learning infrastructure for production use is a significant hurdle for many of our large-scale clients transitioning towards AI-centric operations. This role presents a unique opportunity for a seasoned MLOps engineer or server-side developer to deepen their expertise in this emerging field and spearhead the formation of our inaugural MLOps team, sharing their knowledge across our organization. In the role of MLOps Engineer, you'll be at the forefront of deploying cutting-edge AI solutions for Faktion’s enterprise clients. Consider a scenario where Faktion’s data scientists have developed a groundbreaking system that can automatically interpret and process thousands of images for a major manufacturing plant. It functions flawlessly in a test environment, but the real challenge lies in its deployment to a production setting. How will this system be scaled to handle millions of images? What’s the best approach for users to interact with this system? What tools or platforms should be utilized for ongoing monitoring? As an MLOps Engineer at Faktion, you will navigate these questions and architect the necessary solutions
Some key responsibilities:
Design and build the machine learning pipelines and cloud infrastructure to support our machine learning systems at scale
Take offline models data scientists build and turn them into a real machine learning production system
Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients’ machine learning systems
Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
Implement security best practices to safeguard sensitive data and model outputs, and support model development, with an emphasis on auditability, versioning, and data security
Investigate and resolve issues related to model performance, data pipelines, or infrastructure
Excellent written and verbal communication skills in native-English or native-Dutch (French is a plus).
Experience in setting up CI/CD pipelines (Azure DevOps, GitHub Actions, … )
Experience with message brokers or message services like MS Service Bus, Kafka, RabitMQ or similar solutions
Strong software engineering skills in complex, multi-language systems, with an outspoken fluency in Python
Experience working with cloud computing and database systems
Ability to setup API’s using serverless functions like Azure Functions
Experience developing and maintaining ML systems built with open source tools
Experience developing with containers and Kubernetes in cloud computing environments
Experience with setting up Infrastructure with tools like Terraform or Biceps
Ability to translate business needs to technical requirements
Following experiences are a plus:
Proven track record of developing and deploying scalable machine learning models.
Strong communication and teamwork abilities.
Experience in one of our focus domains: GenAI, Data Quality, Retail, Manufacturing, Finance
Strong understanding of software testing, benchmarking, and continuous integration
Exposure to machine learning methodology and best practices
Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)
We offer:
A rewarding salary package that includes additional perks like a company car and fuel card or a mobility budget, comprehensive hospitalization and group insurance, along with a top-tier laptop and smartphone.
Benefit from a company culture that stimulates both individual and team development, fostering your professional growth.
Utilize your innovation budget for engaging in exciting, educational, and challenging open-source projects within your guild.
Participate in (virtual) team-building activities and gatherings, a great opportunity to unwind and engage with our vibrant team initiatives.
A flexible hybrid working-policy to choose where, how, and when you want to work.
At Faktion we build AI, and we use it thoughtfully in our own processes too. During your application, AI tools may help us with practical steps, like checking that your documents came through complete. They never decide who moves forward: every application is read by a real person on our team, and every decision is made by a human. Curious how this works, or how your data is handled? Just ask, we're happy to explain.