MLOPS ENGINEER

Eviden, part of the Atos Group, with an annual revenue of circa € 5 billion is a global leader in data-driven, trusted and sustainable digital transformation.
As a next generation digital business with worldwide leading positions in digital, cloud, data, advanced computing and security, it brings deep expertise for all industries in more than 47 countries.
By uniting unique high-end technologies across the full digital continuum with world-class talents, Eviden expands the possibilities of data and technology, now and for generations to come.
We seek an experienced and motivated MLOps Engineer to join our team and help us scale our machine learning models and AI solutions effectively.
Job Overview.
The MLOps Engineer will be responsible for streamlining the development, deployment, and monitoring of machine learning models in production.
This role combines machine learning, DevOps, and cloud infrastructure expertise to ensure that our machine-learning pipelines are robust, scalable, and efficient.
Key Responsibilities.
Model Deployment & Automation.
Develop and maintain automated workflows for deploying machine learning models into production, ensuring reproducibility and version control.
Model Monitoring & Maintenance.
Monitor the performance of models in production, ensuring that they continue to deliver high accuracy and reliability over time.
Implement systems for model retraining and updates.
Infrastructure Management.
Build and manage scalable infrastructure for machine learning applications, including data pipelines, CI/CD processes, and cloud environments.
Collaboration with Data Scientists & Engineers.
Work closely with data scientists and software engineers to facilitate the seamless integration of machine learning models into production systems.
Ensure the quality and stability of the code used for machine learning operations.
Performance Optimization.
Optimize the machine learning deployment pipelines for performance, scalability, and cost-effectiveness, using the latest technologies and methodologies.
Security & Compliance.
Implement best practices for security, compliance, and ethical standards related to AI/ML solutions, including data privacy, model explainability, and fairness.
Documentation & Reporting.
Create detailed documentation for MLOps processes, models, infrastructure setups, and operational workflows.
Provide regular reports on model performance and issues.
Skills & Qualifications.
Educational Background.
Bachelor’s or master’s degree in computer science, Data Science, Engineering, or a related field.
Experience.
3+ years of experience in MLOps, DevOps, or a similar role in the data/AI field.
Hands-on experience with deploying machine learning models into production environments.
Proficiency in cloud platforms (e.
., AWS, Azure, GCP) and containerization tools (Docker, Kubernetes).
Strong understanding of version control systems (Git, GitHub, GitLab).
Technical Skills.
Programming languages.
Python, Bash, Java, or similar languages.
Familiarity with machine learning frameworks like TensorFlow, PyTorch, or Scikit-Learn.
Experience with CI/CD pipelines for ML, model versioning, and automation tools (Jenkins, GitLab CI, etc.).
Knowledge of infrastructure-as-code (Terraform, CloudFormation) and cloud-native services for ML (AWS SageMaker, Google AI Platform, etc.).
Soft Skills.
Strong problem-solving and troubleshooting abilities.
Excellent communication skills, with the ability to collaborate across teams.
Ability to work independently and as part of a team in a fast-paced environment.
Passion for continuous learning and staying up to date with the latest trends in machine learning and DevOps.
Preferred Qualifications.
Experience with distributed systems and parallel computing.
Familiarity with ML Ops frameworks like MLflow, Kubeflow, or TFX.
Knowledge of container orchestration and serverless computing models.
Experience in developing scalable data pipelines using Apache Kafka, Apache Airflow, or similar technologies.
Why Join Us?   Training and Certifications.
Access to continuous learning and career development opportunities.
Flexible working environment (remote options available).
Competitive salary and benefits package.
Opportunity to make a real impact in the world of AI and machine learning.
Reimbursement.
Get a yearly fixed amount for reimbursement.
Performance Bonus.
Earn an annual performance bonus based on your achievements.
Career Advancement.
Explore numerous opportunities for professional growth and career advancement.
Extra Vacation Days.
Take advantage of additional vacation days to relax and recharge.
Let’s grow together.