What will you do:

• Design, implement, and maintain scalable and reliable ML infrastructure and pipelines.
• Collaborate with data scientists to deploy machine learning models to production environments.
• Automate the deployment, monitoring, and management of ML models using CI/CD tools such as Git, Jenkins, and Bash.
• Utilize Docker and container orchestration technologies to ensure consistent and reproducible ML environments.
• Manage infrastructure as code using Terraform to provision and manage cloud resources.
• Leverage AWS services (S3, Lambda, EC2, SNS/SQS, IAM) to build and maintain robust ML solutions.
• Develop and maintain scripts and tools in Python (3+) and Java (11+) to support ML workflows.
• Monitor and troubleshoot ML models in production, ensuring high availability and performance.
• Implement security best practices and ensure compliance with industry standards.

What makes you a great fit:

• Experience with ML frameworks and libraries such as TensorFlow, PyTorch, or Scikit-Learn.
• Familiarity with MLOps tools and platforms such as MLflow, Kubeflow, or SageMaker.
• Knowledge of data engineering principles and experience with data pipelines.
• Understanding of software development lifecycle (SDLC) and agile methodologies.
• Strong experience with DevOps practices and tools, including Git, Jenkins, and Bash.
• Proficiency in containerization technologies such as Docker and Kubernetes.
• Hands-on experience with Terraform for infrastructure as code.
• Minimum of 1 year of experience in machine learning operations
• Solid knowledge of AWS services, including S3, Lambda, EC2, SNS/SQS, and IAM.
• Proficiency in Python (3+) and Java (11+).
• Strong problem-solving skills and the ability to troubleshoot complex issues.
• Excellent communication and collaboration skills.

Client: Altimetrik