MLOps Engineer

Job not on LinkedIn

🔥 1 minute ago

🌐 Serbia, Romania – Remote

🇷🇸 Serbia – Remote 🇷🇴 Romania – Remote

⏰ Full Time

🟢 Junior

🟡 Mid-level

🤖 Machine Learning Engineer

👻 Ghost score 18%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Sedona Digital

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Sedona Digital is a process-led technology consultancy that helps enterprises define strategy, build AI- and data-driven products, modernise applications and secure and operate cloud-native environments. They offer services across technology strategy, data architecture and engineering, AI and predictive analytics, software product engineering, cloud migration and Azure managed services, DevOps/CICD, plus security and managed SOC capabilities to deliver ongoing operations and compliance.

📋 Description

• Bridge the gap between Data Science and Infrastructure teams • Support the Data Science team in MLOps-related tasks and assist with DevOps initiatives • Design, implement, and maintain scalable MLOps pipelines on AWS using SageMaker, EC2, EKS, S3, Lambda and other AWS tools • Coordinate with the platform team to troubleshoot Kubernetes clusters (EKS) and orchestrate deployment of machine learning models and microservices • Develop and maintain CI/CD pipelines for model and application deployment, testing, and monitoring • Collaborate with Data Science and DevOps teams to streamline the model development lifecycle from experimentation to production deployment • Implement security best practices, including network security, data encryption, and role-based access controls in AWS infrastructure • Monitor, troubleshoot, and optimize data and ML pipelines for high availability and performance • Set up and manage model monitoring systems for performance drift and continuous model improvement • Provision cloud resources using infrastructure-as-code tools such as Terraform

🎯 Requirements

• Bachelor’s degree in Computer Science, Engineering, or related field • 2+ years of hands-on experience in MLOps, DevOps, or related fields • Knowledge and preferable working experience in AWS services for machine learning, such as SageMaker, EKS, S3, EC2, Lambda, and others • Exposure to Kubernetes for container orchestration • Experience with Docker • Exposure to infrastructure-as-code tools such as Terraform or CloudFormation • Familiarity with CI/CD tools such as GitLab CI • Understanding of the machine learning model lifecycle • Familiarity with monitoring and logging solutions like Prometheus, Grafana, CloudWatch and ELK Stack • Understanding of networking concepts and cloud security best practices • Proficiency in Python and Bash, and comfortable working in Linux environments • Strong problem-solving and communication skills • Advantage: experience with serverless architectures and event-driven processing on AWS • Advantage: familiarity with advanced Kubernetes concepts such as Helm • Advantage: experience with Data Engineering pipelines, ETL processes, or big data platforms • Advantage: experience with ML frameworks like TensorFlow, PyTorch and Keras • Advantage: experience with ML platforms like Kubeflow and/or SageMaker • Advantage: experience with workflow engines like Argo Workflows and/or Airflow

🏖️ Benefits

• Work from home • Medical insurance given after the probation period • Training on market trends and client needs • Continuous learning and personal development opportunities • Holiday allowance in line with local legislation • Competitive compensation package • Performance-based bonus • Fun budget for team events

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