Asks for

The ad says“Hands-on with a major cloud platform (Azure, AWS, or GCP).”
The ad says“Hands-on with a major cloud platform (Azure, AWS, or GCP).”
The ad says“Design and maintain CI/CD pipelines for ML models and services.”
The ad says“Builds and maintains the infrastructure and tooling that keeps machine learning systems reliable in production — from designing CI/CD pipelines and model deployment workflows to monitoring performance and managing model lifecycle at scale. The role works closely with ML, backend, and platform teams, contributes to automation frameworks and observability standards, and helps ensure AI models move seamlessly from experimentation to production. Requires 5+ years of MLOps or DevOps engineering experience, with a track record of operating robust ML infrastructure in production-grade environments.”
The ad says“Strong with containers and orchestration (Docker, Kubernetes).”
The ad says“Hands-on with a major cloud platform (Azure, AWS, or GCP).”
The ad says“Experience with CI/CD (GitLab CI) and infrastructure-as-code (Terraform or similar).”
The ad says“Familiarity with ML frameworks (PyTorch), workflow orchestration (Kubeflow or similar), and monitoring stacks.”
The ad says“Strong with containers and orchestration (Docker, Kubernetes).”
The ad says“Hands-on experience with MLflow for experiment tracking and model registry.”
The ad says“Builds and maintains the infrastructure and tooling that keeps machine learning systems reliable in production — from designing CI/CD pipelines and model deployment workflows to monitoring performance and managing model lifecycle at scale. The role works closely with ML, backend, and platform teams, contributes to automation frameworks and observability standards, and helps ensure AI models move seamlessly from experimentation to production. Requires 5+ years of MLOps or DevOps engineering experience, with a track record of operating robust ML infrastructure in production-grade environments.”
The ad says“Familiarity with ML frameworks (PyTorch), workflow orchestration (Kubeflow or similar), and monitoring stacks.”
The ad says“Experience with CI/CD (GitLab CI) and infrastructure-as-code (Terraform or similar).”

Our mission

Constructor’s mission is to enable all educational organisations to provide high-quality digital education to 10x people with 10x efficiency.

With strong expertise in machine intelligence and data science, Constructor’s all-in-one platform for education and research addresses today’s pressing educational challenges: access inequality, tech clutter, and low engagement of students.

Please send your resume in English only.

Brief Job Description:

Builds and maintains the infrastructure and tooling that keeps machine learning systems reliable in production — from designing CI/CD pipelines and model deployment workflows to monitoring performance and managing model lifecycle at scale. The role works closely with ML, backend, and platform teams, contributes to automation frameworks and observability standards, and helps ensure AI models move seamlessly from experimentation to production. Requires 5+ years of MLOps or DevOps engineering experience, with a track record of operating robust ML infrastructure in production-grade environments.

Mission:

Make the path from model experiment to reliable production as fast, automated, and observable as possible.

Education:

Bachelor’s degree or higher in Computer Science, Engineering, or a related field — or equivalent practical experience.

Duties and Responsibilities:

Design and maintain CI/CD pipelines for ML models and services.
Build and operate model deployment, serving, and rollback workflows.
Implement monitoring, observability, and alerting for models in production.
Manage the model lifecycle with MLflow: experiment tracking, versioning, registries, and reproducibility.
Automate infrastructure and partner with ML and platform teams on standards.

Qualifications & Experience:

5+ years of MLOps or DevOps engineering experience.
Strong with containers and orchestration (Docker, Kubernetes).
Experience with CI/CD (GitLab CI) and infrastructure-as-code (Terraform or similar).
Hands-on with a major cloud platform (Azure, AWS, or GCP).
Hands-on experience with MLflow for experiment tracking and model registry.
Familiarity with ML frameworks (PyTorch), workflow orchestration (Kubeflow or similar), and monitoring stacks.

Nice to Have (Not Obligatory):

Experience serving LLMs or large models in production.
Knowledge of feature stores and data pipeline tooling.
Cost and latency optimisation for model serving.

What We Offer

💻 Choice of work equipment (e.g., laptop, monitor, etc.)
🇬🇧 English classes (iTalki – $130 monthly)
⏰ Flexible schedule (we usually work between 09:00/10:00 and 18:00/19:00)
👶 Newborn bonus (€500 per child)
🧠 Patent remuneration
🌴 Paid leave
🧑‍💻 Remote work in locations without our offices
Hybrid work in locations with offices (2 days in-office, 3 days remote)

Constructor fosters equal opportunity for people of all backgrounds and identities. We are led by a gender-balanced board committed to building a diverse and inclusive organisation where everyone can become their best self. We do not discriminate based on age, disability, gender identity, sexual orientation, ethnicity, race, religion or belief, parental and family status, or other protected characteristics. We welcome applications from women, men and non-binary candidates of all ethnicities and socio-economic backgrounds. We encourage people belonging to underrepresented groups to apply.