Key Facts

Remote From:

Full time

Senior (5-10 years)

English

Hard Skills

Docker (Software) MLOps (Machine Learning Operations) Kubernetes Python (Programming Language) System Monitoring Containerization Application Programming Interface (API) Software Development Data Architecture Notification Services +21 more

Other Skills

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Collaboration
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Communication
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Teamwork
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Mentorship
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Problem Solving

Roles & Responsibilities

5+ years of experience in software engineering, DevOps, or MLOps roles
Strong Python programming skills (familiarity with Java/Go is a plus)
Hands-on experience with Docker and Kubernetes
Experience with cloud platforms (AWS, GCP, or Azure) and CI/CD tools (GitHub Actions, Jenkins, GitLab CI)

Requirements:

Design, build, and maintain scalable ML pipelines for training, testing, deployment, and monitoring
Deploy and manage ML models, ensuring performance, reliability, observability, and data-drift monitoring
Implement ML CI/CD and manage cloud-based infrastructure for ML workloads
Collaborate with data scientists and engineers to streamline experimentation and deployment workflows; ensure reproducibility and governance of models and datasets; advocate for MLOps best practices

Job description

Robusta assists organizations in transitioning to a digital-first approach, crafting unforgettable experiences for their customers. We provide strategy, design, product, and technology services to prominent businesses and brands, utilizing our go-to-market expertise to facilitate seamless customer experiences and enhance conversion rates.

We’re looking for a Senior MLOps Engineer to lead the design, deployment, and scaling of machine learning systems in production. You’ll work at the intersection of data science, software engineering, and infrastructure to ensure reliable, efficient, and scalable ML pipelines. This role is ideal for someone who thrives in building robust systems and enabling teams to move faster with high-quality ML workflows.

Responsibilities

Design, build, and maintain scalable ML pipelines for training, testing, and deployment
Deploy & maintain machine learning models and ensure their performance, reliability, and monitoring
Collaborate with data scientists and engineers to streamline experimentation and deployment workflows
Implement CI/CD practices for ML systems (ML CI/CD)
Manage and optimize cloud-based infrastructure for ML workloads
Develop monitoring, logging, and alerting systems for model performance and data drift
Ensure reproducibility, versioning, and governance of ML models and datasets
Advocate for best practices in MLOps, DevOps, and software engineering

Requirements

5+ years of experience in software engineering, DevOps, or MLOps roles
Strong programming skills in Python (and familiarity with Java/Go is a plus)
Experience with ML frameworks such as TensorFlow, PyTorch, or similar
Hands-on experience with containerization and orchestration tools (Docker, Kubernetes)
Experience with cloud platforms (AWS, GCP, or Azure)
Familiarity with CI/CD tools (e.g., GitHub Actions, Jenkins, GitLab CI)
Strong understanding of data pipelines, distributed systems, and API development
Experience with monitoring tools and logging frameworks