MLFlow – Who Needs Engineers
/wne_live
Software Engineering RecruitmentSun, 13 Oct 2024 16:00:02 +0000en-US
hourly
1 https://wordpress.org/?v=6.6.2/wne_live/wp-content/uploads/2023/06/cropped-wne_logo-3-32x32.pngMLFlow – Who Needs Engineers
/wne_live
3232Machine Learning Automation Engineer
/jobs/emerging-technologies-and-specialized-roles/machine-learning-automation-engineer/
Sun, 13 Oct 2024 16:00:02 +0000/wne_live/jobs/jobs-categories/machine-learning-automation-engineer/Machine Learning Automation Engineer
Job Description:
Are you passionate about automation and streamlining processes? Do you find repetitive tasks tedious and enjoy creating innovative solutions that drive efficiency? If you have a knack for quantifying and measuring results, we want you to join our team. In this role, you will be a key player in automating machine learning operations and enhancing our MLOps framework. Our innovative technology company is transforming the real estate sector with cutting-edge digital solutions aimed at efficiency and sustainability. As a Machine Learning Automation Engineer, you will work closely with cross-functional teams, enabling the seamless deployment of scalable machine learning models that make meaningful contributions to our projects. Your work will focus on designing and implementing automated ML pipelines, ensuring that our clients receive the highest quality service while optimizing refurbishment projects. This position offers a unique opportunity to be part of a dynamic startup that is making waves in the industry.
IT Languages:
Python
SQL
As a Machine Learning Automation Engineer, your primary responsibilities will include the design and implementation of automated ML workflows and pipelines to enhance our machine learning capabilities.:
Design and implement automated ML pipelines for data preprocessing, model training, evaluation, and deployment.;; Collaborate with data scientists and engineers to integrate ML models into production environments, ensuring optimal performance and scalability.;; Develop and maintain CI/CD pipelines for machine learning projects to automate model testing, deployment, and monitoring.;; Implement best practices in MLOps, including version control and performance tracking.;; Work with the data engineering team to ensure high-quality data availability for model training and inference.;; Stay updated on advancements in AI/ML technologies and automation tools to enhance our ML infrastructure.
Spoken Languages:
English;; German
Skillset:
CI/CD
Docker
Kubernetes
AWS
GCP
Azure
Scikit-learn
TensorFlow
PyTorch
Kubeflow
Airflow
MLflow
Postgres
BigQuery
Oracle
Soft Skills:
Excellent problem-solving abilities
Strong communication skills
Ability to work independently and as part of a team
Qualifications:
Strong understanding of CI/CD principles
Experience with containerization technologies
Proficiency in Python and familiarity with ML libraries
Expertise in automation/orchestration tools
Experience with database management systems
Years of Experience:
3
Location:
Germany
Job Benefits:
Competitive salary
Comprehensive benefits package
Flexible working arrangements including remote options
Opportunities for continuous learning and personal growth
Collaborative and innovative work environment
Working Conditions:
Full Time
Employment Type:
Permanent Contract
Company Culture:
At our company, we foster a culture of creativity and innovation, with an emphasis on teamwork and collaboration. Our flat hierarchies allow for rapid decision-making and a dynamic work environment where employees are valued and encouraged to contribute ideas.
Opportunities For Advancement:
Professional development programs, Mentorship opportunities, Room for career progression within the company
We are seeking a skilled Machine Learning Operations Engineer to join our team within the Central Data Platforms & AI division. In this role, you will be responsible for the design, implementation, and monitoring of the infrastructure and processes that are essential for efficiently operating and scaling machine learning models in our production environments. This full-time position allows for a hybrid working model, with opportunities to work remotely on select days. Your contributions will directly influence the effectiveness and reliability of our machine learning applications, ensuring they meet the demands of our business objectives.
IT Languages:
Python
SQL
Java
As a Machine Learning Operations Engineer, you will play a pivotal role in the deployment and management of machine learning models, ensuring their efficiency, scalability, and robustness.:
Design, create, and automate the management of the machine learning model lifecycle;; Collaborate closely with Data Engineers and Scientists to streamline processes;; Optimize deployment processes using MLOps and DevOps principles;; Establish robust monitoring systems to assess model performance and data changes
Spoken Languages:
English;; German
Skillset:
MLFlow
Azure DevOps
Databricks
CI/CD
Gitflow
Container Management
Infrastructure as Code
PyTorch
TensorFlow
Soft Skills:
Analytical thinking
Proactive problem-solving
Quality-oriented work ethic
Professional demeanor
Qualifications:
Degree in Computer Science, Data Science, Statistics, Mathematics, Physics, or a related field
Strong experience in deploying and managing machine learning models in production environments
Familiarity with DevOps and MLOps principles
Years of Experience:
5
Location:
Göttingen, Lower Saxony, Germany, EU
Job Benefits:
Room for personal and professional development through various programs
Flexible working arrangements including home office options
Attractive compensation package with additional benefits
Modern campus amenities including open space offices and fitness facilities
Comprehensive onboarding with support from a designated buddy
Working Conditions:
Hybrid
Employment Type:
Permanent Contract
Company Culture:
We foster a collaborative and innovative environment where team players and creative minds can thrive. Our focus is on personal growth and making a meaningful impact in the fight against diseases.