MLOps Engineer

Sedona Digital; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Work from home

Medical insurance after probation

Training on market trends

Continuous learning opportunities

Holiday allowance

Performance-based bonus

Team events budget

Job summary

Sedona Digital ищет ML/MLops инженера для поддержки жизненного цикла моделей и инфраструктуры в облаке. Ваша задача — проектировать, разворачивать и поддерживать масштабируемые ML‑пайплайны на AWS, сотрудничать с Data Science и DevOps.

Требуется 2+ года опыта в MLOps/DevOps, владение Python, Docker, Kubernetes и Terraform, опыт работы с SageMaker и EKS, знание GitLab CI. Предоставляется гибкая настройка работы и рост квалификации.

Qualifications

• Образование: бакалавр по CS или смежной дисциплине.
• Опыт 2+ лет в MLOps/DevOps или близких областях.
• Опыт работы с AWS для ML (SageMaker, EKS, S3, EC2, Lambda).
• Опыт Kubernetes и Docker; IaC‑инструменты (Terraform, CloudFormation).
• Опыт CI/CD (GitLab CI).
• Знание Python и Bash; Linux‑окружение.

Responsibilities

• Проектирование и поддержка масштабируемых ML‑pipeline на AWS (SageMaker, EKS, S3).
• Разработка и поддержка CI/CD процессов для разворачивания моделей и сервисов.
• Работа с командами Data Science и DevOps над жизненным циклом модели.
• Обеспечение безопасности, мониторинга и устойчивости решений.

Skills

MLOps

Python

Docker

Kubernetes

CI/CD

Terraform

Linux

Education

Bachelor’s degree in Computer Science or related field

Tools

SageMaker

EC2

EKS

S3

Lambda

GitLab CI

Job description

Описание

Join Sedona Digital, a fast-growing scale-up organization with an ambition to be recognized as one of the leading technology companies servicing high-tech, global enterprises across Technology, Finance, and Life Sciences sectors. Our global client base needs builders: engineers and developers who love technology, have deep expertise in software, engineering, and cloud technologies, and importantly, have a passion for culture and customers. - We obsess about our customers - We build outstanding technical solutions - We create an awesome culture - We accelerate learning and careers We are looking for a skilled MLOps Engineer to join our team and play a key role in bridging the gap between our Data Science and Infrastructure teams. You will be responsible for supporting the Data Science team in MLOps-related tasks while also helping in DevOps initiatives, including CI/CD pipeline creation, provisioning of cloud resources using tools like Terraform, and Kubernetes orchestration. You will collaborate closely with data scientists and engineers to deploy data pipelines, train machine learning models, and manage their deployment within scalable cloud environments while ensuring high performance, security, and reliability throughout the ML lifecycle.

Задачи

Assist in designing, implementing, and maintaining scalable MLOps pipelines on AWS using services such as SageMaker, EC2, EKS, S3, Lambda and other relevant AWS tools Coordinate with our platform team to troubleshoot Kubernetes clusters (EKS) to orchestrate the deployment of machine learning models and other microservices Develop and maintain CI/CD pipelines for model and application deployment, testing, and monitoring Collaborate closely with Data Science, and DevOps team 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 within the AWS infrastructure Monitor, troubleshoot, and optimize data and ML pipelines to ensure high availability and performance Set up and manage model monitoring systems for performance drift, ensuring continuous model improvement

Требования

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 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 The following will be considered an advantage: Experience working with serverless architectures and event-driven processing on AWS Familiarity with advanced Kubernetes concepts such as Helm Experience with Data Engineering pipelines, ETL processes, or big data platforms Experience with ML frameworks like TensorFlow, PyTorch and Keras Experience with ML platforms like Kubeflow and/or SageMaker Experience with workflow engines like Argo Workflows and/or Airflow

Условия

• 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