Remote MLOps Engineer: Scale Production ML Pipelines

Hitachi Vantara Corporation; Poland; Remote; PLN 255,000 - 342,000; Full time

Job summary

A leading digital engineering partner is looking for a Machine Learning Operations Engineer to deploy and monitor machine learning models effectively. You will bridge the gap between platform engineering and software, automate workflows, and optimize performance. Required skills include proficiency in Python, experience with cloud platforms (AWS, GCP), and knowledge of MLOps frameworks. This role is crucial for enhancing the scalability of machine learning solutions.

Benefits

Learning and development opportunities

Flexible work arrangements

Opportunity to work on impactful projects

Qualifications

• Proficiency in Python for ML development.
• Expertise in cloud platforms (AWS, GCP).
• Strong knowledge of MLOps frameworks and DevOps tools.
• Experience with Docker and Kubernetes.
• Hands-on experience with ML frameworks.

Responsibilities

• Deploy, monitor, and maintain machine learning models.
• Automate model training and governance processes.
• Design and implement scalable MLOps pipelines.
• Work closely with data scientists to operationalize ML models.

Skills

Proficiency in Python

Familiarity with Clojure

Cloud platforms (AWS, GCP)

Knowledge of MLOps frameworks

DevOps tools (Jenkins, GitLab)

Experience with Docker

Experience with Kubernetes

Infrastructure-as-code tools (Terraform)

SQL/NoSQL databases

Strong communication skills

Problem-solving mindset

Education

5+ years in platform or MLOps engineering

Tools

TensorFlow

PyTorch

Job description

A leading digital engineering partner is looking for a Machine Learning Operations Engineer to deploy and monitor machine learning models effectively. You will bridge the gap between platform engineering and software, automate workflows, and optimize performance. Required skills include proficiency in Python, experience with cloud platforms (AWS, GCP), and knowledge of MLOps frameworks. This role is crucial for enhancing the scalability of machine learning solutions.