MLOps Engineer
4 weeks ago
About us:
Intuitive.Cloud is one of the fastest-growing (INC 5000, CRN) Cloud & SDx solution and services companies supporting enterprise customers on a global scale. Intuitive is an "Engineering Company" delivering measurable value and key business outcomes.
Intuitive Superpowers:
- DataOps & AI/ML
- Cloud Native, AppSecOps, DevSecOps
- Cloud Migration & Transformation
- Cloud FinOps
- Cybersecurity (App/Data/Infra) & GRC
- SDx & Digital Workspace
We are proud to partner with some of the world's leading enterprises and serve 200+ customers across different industry verticals. We have achieved many milestones along the way, including being recognized as a top-10 fast-growth 150 IT company in the Americas by CRN in 2022 and being named one of America's fastest-growing private companies by INC 5000 in 2022. That’s not all Even CIO Review awarded us as the Most Promising Cloud Migration Company and Artificial Intelligence Solutions Provider in 2022.
Title – Mid Level ML Engineer
Start date: Immediate
Position Type: Full-Time
Location: Remote across India
Work Hours: Eastern Time Zone
Role Overview:
We seek a skilled MLOps Engineer to join our dynamic team and play a pivotal role in building and maintaining machine learning pipelines across cloud platforms such as AWS, Azure, or GCP. As an MLOps Engineer, you will be responsible for deploying and managing machine learning pipelines, including implementing data ingestion, data cleaning, feature engineering, training, deployment, and monitoring strategies, and ensuring the reliability and scalability of our AI infrastructure.
Key Responsibilities:
- build, and optimize machine learning pipelines on cloud platforms such as AWS, Azure, or GCP.
- with data scientists and engineers to deploy machine learning models into production environments.
- continuous integration and deployment (CI/CD) pipelines to automate model training, testing, and deployment processes.
- and optimize the performance of machine learning models in production and implement strategies for model retraining and versioning.
- the security, reliability, and scalability of machine learning infrastructure and environments.
Required Skills and Qualifications:
- technical skills in model development in the machine learning and deep learning space.
- building and deploying machine learning pipelines on cloud platforms such as AWS, Azure, or GCP.
- Proficiency in programming languages such as Python, Java, or Scala.
- Familiarity with containerization technologies such as Docker and orchestration tools like Kubernetes.
- Knowledge of DevOps practices and tools for automation, such as Jenkins or GitLab.
- Excellent problem-solving and communication skills.
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