Associate Principal Scientist, Senior Machine Learning Engineer

Jobtailor; Wien; Vor Ort; EUR 70.000 - 110.000; Vollzeit

Zusammenfassung

Jobtailor is seeking an ML Engineer/MLOps specialist to build and own robust ML lifecycle tooling, pipelines, and model deployment across biomedical applications. You will work with AWS/SageMaker, Databricks, and Docker to manage compute, track experiments, and ensure reproducible, scalable development.

The role emphasizes collaboration with data engineering and AI science teams, transforming prototypes into reusable solutions and advancing model fine‑tuning and evaluation as the platform

Qualifikationen

• Degree in Computer Science, Engineering, or a related field, with hands‑on experience as an ML Engineer, MLOps Engineer, or in a similar role.
• Strong software engineering skills in Python, with proven experience deploying, serving, and maintaining ML/DL models in production environments.
• Demonstrated MLOps expertise, including reproducible pipelines, experiment tracking, model versioning and registries, containerization (Docker), and cloud‑based ML infrastructure (AWS, Azure, or similar platforms).
• Strong interest and aptitude in Machine Learning and Deep Learning, with hands‑on experience training and fine‑tuning models and a desire to grow further in applied ML/DL development.
• Experience or interest in scientific, pharmaceutical, or biomedical applications is a strong advantage, although deep domain expertise is provided by other members of the team.

Aufgaben

• Build and own the team’s ML lifecycle tooling, including reproducible training and fine‑tuning pipelines, experiment tracking, model registries, and the packaging and deployment of deep learning models.
• Drive efficient and reproducible model development by managing compute environments, GPU workloads, and large‑scale biomedical datasets on cloud platforms such as AWS/SageMaker and Databricks.
• Transform prototype code into robust, reusable solutions, establishing frameworks, templates, and best practices that accelerate delivery across projects.
• Collaborate closely with data engineering, platform, and AI science teams to integrate ML solutions into the broader Computational Innovation ecosystem.
• Evolve from ML platform ownership toward applied ML/DL development, contributing increasingly to model adaptation, fine‑tuning, evaluation, and the delivery of AI solutions for scientific use cases as the platform matures.

Kenntnisse

Machine Learning

MLOps Expertise

Python Programming

Cloud Platforms

Model Deployment

Ausbildung

Degree in Computer Science or Engineering

Tools

AWS

SageMaker

Databricks

Docker

Azure

Jobbeschreibung

• Build and own the team’s ML lifecycle tooling, including reproducible training and fine‑tuning pipelines, experiment tracking, model registries, and the packaging and deployment of deep learning models.


• Drive efficient and reproducible model development by managing compute environments, GPU workloads, and large‑scale biomedical datasets on cloud platforms such as AWS/SageMaker and Databricks.


• Transform prototype code into robust, reusable solutions, establishing frameworks, templates, and best practices that accelerate delivery across projects.


• Collaborate closely with data engineering, platform, and AI science teams to integrate ML solutions into the broader Computational Innovation ecosystem.


• Evolve from ML platform ownership toward applied ML/DL development, contributing increasingly to model adaptation, fine‑tuning, evaluation, and the delivery of AI solutions for scientific use cases as the platform matures.

Requirements

• Degree in Computer Science, Engineering, or a related field, with hands‑on experience as an ML Engineer, MLOps Engineer, or in a similar role.


• Strong software engineering skills in Python, with proven experience deploying, serving, and maintaining ML/DL models in production environments.


• Demonstrated MLOps expertise, including reproducible pipelines, experiment tracking, model versioning and registries, containerization (Docker), and cloud‑based ML infrastructure (AWS, Azure, or similar platforms).


• Strong interest and aptitude in Machine Learning and Deep Learning, with hands‑on experience training and fine‑tuning models and a desire to grow further in applied ML/DL development.


• Experience or interest in scientific, pharmaceutical, or biomedical applications is a strong advantage, although deep domain expertise is provided by other members of the team.

Core Competencies

Demonstrates expertise in Machine Learning and Deep Learning, with a strong foundation in Python programming and MLOps practices. Capable of building and managing ML lifecycle tooling, deploying models in production, and collaborating across teams to deliver AI solutions for scientific applications.

Highest-signal resume keywords

• Machine Learning Lifecycle Tooling


• MLOps Expertise


• Python Programming


• Cloud-Based ML Infrastructure


• Model Deployment and Maintenance

ATS Optimization Keywords

Hard Skills

• Machine Learning


• Deep Learning


• MLOps


• Experiment Tracking


• Model Versioning


• Containerization


• Reproducible Pipelines


• Model Registries


• Fine-Tuning Models


• Data Engineering

Industry Keywords

• Biomedical Applications


• Pharmaceutical Applications


• Computational Innovation


• Cloud Platforms

Tools & Technologies

• AWS


• SageMaker


• Databricks


• Docker


• Azure