
Machine Learning Engineer@ING Hubs Romania
at ING Bank
Posted 6 days ago
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- Compensation
- Not specified
- City
- Bucharest
- Country
- Romania
Currency: Not specified
The role is for a Machine Learning Engineer to help set new standards in model development and productionizing ML on Google Cloud using Python in an agile environment. You will bridge data science and production engineering by turning prototypes into reliable, high-performance solutions and contribute to agentic AI capabilities including tool calling and orchestration. You will design and maintain end-to-end ML pipelines with Vertex AI Pipelines, support the Google Cloud infrastructure, participate in CI/CD, and collaborate with Data Scientists and stakeholders to align technical solutions with business needs.
Discover ING Hubs Romania
ING Hubs Romania offers 130 services in software development, data management, non-financial risk & compliance, audit, and retail operations to 24 ING units worldwide, with the help of over 2000 high-performing engineers, risk, and operations professionals.
We started out in 2015 as ING’s software development hub, then steadily expanded our range to include more services and competencies. Now we provide borderless services with bank-wide capabilities and operate from two locations: Bucharest and Cluj-Napoca.
Our tech capabilities remain the core of our business, with more than 1800 colleagues active in Data and Analytics Tech, Tech Foundation and Channels, Retail Core Banking and Architecture, and Global Products and Technology Services.
We enjoy a flexible way of working and a highly collaborative environment, where fair and constructive feedback is encouraged.
For us, impact isn't a perk. It's the driver of our work. We are guided and rewarded by a shared desire to make the world a better place, one innovative solution at a time. Our colleagues make it their job to do impactful things and they love doing it in good company. Do you?
The Mission
We are looking for a strong development expert with a passion for machine learning engineering who can help us set new standards in model development and in maintaining our Google Cloud architecture using Python within an agile environment. You will also help shape our agentic AI capabilities, including toolcalling and orchestration where possible. In this role, you will bridge the gap between data science and production engineering by transforming prototypes into reliable, highperformance solutions.
Your day to day
Working together with a Data Scientist to design and maintain endtoend ML pipelines using Vertex AI Pipelines, including data preprocessing, training, evaluation, and deployment steps.
Support the platform team maintaining Google Cloud Infrastructure resources & programs according to a set of coding standards.
Participate in technical analysis design & estimate duration of programming and unit testing phase for the tooling.
Be part of a CI/CD environment.
Work with: Python, VertexAI, DataProc, Azure DevOps.
Keep the technical documentation up-to-date with new/changed technical design details during implementation & provide technical insight, actively participate in feature analysis.
You take responsibility of your code, from IDE to local development environment to production.
Perform unit testing and register tests results after implementation to assure that every component of the application that was added or changed is working properly.
A modern & agile environment.
Provide workshops & trainings to Data Scientists and Data Analysts.
Set standards in MLE world and drive best practices.
Communicate effectively with stakeholders to ensure alignment between technical solutions and business needs.
What you bring to the team
Solid understanding of Python
Solid understanding of OOP concept
The strong foundational knowledge and ability to adapt to new technologies and paradigms that are often found in development experts are considered more important than most missing nice to haves in this list
Experience working with infrastructure as a code
Basic understanding of ML frameworks (Pandas, Numpy, SciKit-Learn)
Experience with Python, Spark, VertexAI, DataProc, Azure DevOps, Linux, Jupyter Notebooks, BigQuery, Google Cloud Storage
Familiarity with agentic AI architectures, including tools/workflows for autonomous agents, toolcalling, reasoning frameworks, or multi-step orchestrations.
Experience working within Continuous Delivery or Continuous Deployment processes
Understanding of the benefits of unit/integration testing discipline
Superb analytical and problem-solving abilities
Good communication skills, awareness towards emotional intelligence factors and excellent ability to speak & write English
Nifi and Kafka knowledge is a plus
+5 years of experience
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