Senior Machine Learning Engineer at Xebic

, , Romania -

Full Time

Start Date

Immediate

Expiry Date

06 Oct, 26

Salary

0.0

Posted On

08 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, MLOps, Google Cloud Platform, Vertex AI, Docker, Kubernetes, Terraform, GitHub Actions, CI/CD, Model Monitoring, Feature Store, ML Pipelines

Industry

IT Services and IT Consulting

Description

Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions. We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture. In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing. You will be: building ML pipelines by designing and operating reproducible training and deployment pipelines on Vertex AI (Pipelines, Model Registry, Endpoints), owning the feature store, ensuring features are defined once, reused across teams, and remain consistent between training and serving, automating the model lifecycle through CI/CD, testing, and controlled rollouts, enabling Data Scientists to deploy models safely with minimal handoffs, monitoring models in production by implementing model and data monitoring, including drift detection and business-impact performance alerts, so issues are identified before they affect customers, ensuring data and model lineage by versioning datasets and feature definitions in Git, keeping ML assets traceable and reproducible, working hand in hand with Data Scientists and Google Cloud engineers to advance our MLOps maturity from ad hoc processes to automated, scalable workflows. Your profile: 3+ years of experience in ML Engineering, MLOps, Platform Engineering, or Data/Infrastructure roles supporting production machine learning, strong Python skills and solid software engineering fundamentals, including testing, version control, and code reviews, hands-on experience with cloud platforms, ideally Google Cloud Platform and Vertex AI (Pipelines, Feature Store, Model Registry, Model Monitoring), fluency with containers, CI/CD, and infrastructure as code (Docker, Kubernetes, Terraform, GitHub Actions, or similar), experience with model monitoring, observability, and drift detection in production environments. Work from the European Union region and a work permit are required. Nice to have: experience with feature stores (Vertex AI Feature Store, Feast) and orchestration (Kubeflow, Airflow), background in high-throughput, low-latency systems such as real-time bidding or adtech, interest in the ML foundations that make agentic AI dependable - clean data, observability, and reliable serving. Recruitment Process: CV review – HR call – Interview – Client Interview – Decision

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Responsibilities

Design and operate reproducible ML pipelines and feature stores using Vertex AI to automate the model lifecycle. Collaborate with Data Scientists and engineers to implement model monitoring, drift detection, and scalable MLOps workflows.