Data Engineer for ML Ops Area

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In Data Science Platform Squad, we Build and maintain a generic, scalable and govern platform for Data Science model of online models to enable an automated, compliant and efficient Model Life Cycle process. We use technologies like Open-Shift to build Containerized platform for Hosting Model execution & Deployment.

Our squad is responsible for:

Hosting Python statistical and machine learning models. The solution is primarily serving the Data Science communities who develop the models, on behalf of the many enterprise domains in the bank.
AAPF (Advanced Analytical Platform for Fraud) is a platform in conjunction with CMDEP (Containerized Management Deployment and Execution platform) is designed to execute machine learning models to score payment transactions for fraud. Payment transactions include e-banking domestic/ international & District International models deployed in CMDEP.
Shaping the current On-Prem infrastructure into a Cloud-Native platform.

As a MLOps Data Engineer, you will work closely with the Product Owner and Chapter Lead in a “2 in a box” model, using agile methodologies. You will be responsible to develop and maintain scalable ML pipelines on futured MLOps platform built on AWS and Databricks. You will be tasked with ensuring seamless and automated transition of models from development to production using CICD workflows, implement the infrastructure as code. You will ensure that our machine learning models operate reliably, efficiently and at scale.

You will:

Design and implement automated pipelines for model training , validation and inference
Build and maintain CI/CD workflows using Github actions to ensure model code is versioned and seamlessly deployed to production
Utilize tools like MLflow for model tracking, versioning and ensuring reproducibility and compliance across the ML lifecycle
Use terraform to provision and manage scalable secure infrastructure required for ML workloads
Leverage docker containers to build, deploy and manage containerized ML services for both batch and real time inference
Work closely with data scientist, product owner and chapter lead with inn your tribe to understand model requirement and integrate the feedback into continuous MLOps pipelines
Follow the MLOps best practices and focused on automating ML workflows, troubleshooting production issues and optimizing the resources usage

About you:

Minimum of 2+ years of practical experience in MLOps, DevOps and Data Engineering role with focus on ML
In-depth knowledge of AWS services and Databricks, with a background in creating and managing ML pipelines
Proficiency in Python, Pyspark and strong SQL skills
Experience in CI/CD tools terraform and container orchestration
Problem solving and debugging skills
Solid understanding and practical experience with PubSub and Kafka streaming services
Upper-Intermediate English skills

We offer:

Monthly salary range from 3120 EUR to 4680 EUR gross (based on your competencies relevant for the job).

Additionally, each Danske Bank employee receives employee benefits package which includes:

Growth opportunities: professional & supportive team, e-learnings, numerous development programs; (incl. professional certificates); 100+ professions for internal mobility opportunities.
Health & Well-being: a diverse, inclusive, work & life balance work environment; additional health insurance; mental well-being practices; partial psychologist counselling compensation; silence and sleep zones at the office; game rooms.
Hybrid working conditions: Work from home up to two days a week; home office budget (after the probation period); modern Danske Campus workplace developed with anthropologist for the best employee experience.
Additional days of leave: for rest, health, volunteering, exams in higher education institutions, and other important activities. Moreover, for seniority with Danske Bank.
Monetary compensation package: accidents & critical diseases insurance; financial support in case of unfortunate events, travel insurance; IIIrd Pillar Pension Fund contribution.