Machine Learning Threat Intelligence Engineer
Role details
Job location
Tech stack
Job description
- Work closely with data science, DevOps, and engineering teams to support AI/ML infrastructure, deployments, and cloud-based initiatives.
Day to Day Responsibilities
- Design, deploy, and manage cloud infrastructure supporting AI/ML workloads on AWS and Azure
- Manage compute resources including EC2, Azure Virtual Machines, GPU instances, and Kubernetes clusters
- Provision and configure storage, networking, and security services for AI platforms
- Deploy and maintain AI/ML services such as Amazon SageMaker and Azure Machine Learning
- Support data scientists and ML engineers with optimized environments for model training and deployment
- Implement Infrastructure as Code (IaC) using Terraform, CloudFormation, and ARM/Bicep templates
- Automate provisioning, scaling, and patching of cloud environments
- Deploy and manage containerized applications using Docker, Kubernetes, EKS, and AKS
- Monitor system performance using tools like CloudWatch, Azure Monitor, Datadog, and Prometheus
- Optimize infrastructure for cost efficiency, performance, and GPU utilization
- Implement security best practices including IAM/RBAC, encryption, and network security
- Ensure compliance with organizational and regulatory standards
- Integrate AI/ML infrastructure with CI/CD pipelines for automated deployments
Requirements
Do you have experience in Terraform?, * Experience as a Cloud Engineer / AI-ML Infrastructure Engineer
- Strong hands-on experience with AWS and Microsoft Azure
- Experience supporting AI/ML platforms and cloud-based data environments
- Proficiency in Linux administration and scripting (Python, Bash, PowerShell)
- Hands-on experience with Docker, Kubernetes, and container orchestration
- Experience with Infrastructure as Code tools like Terraform or CloudFormation
- Experience with monitoring and performance optimization tools
Preferred Skills & Qualifications
- If possible someone who has worked on GPU-based infrastructure for AI workloads
- Knowledge of MLOps practices and ML pipelines
- Experience with data platforms such as Snowflake, Databricks, or Spark
- Familiarity with AI frameworks like TensorFlow or PyTorch
- Cloud certifications such as AWS Certified Solutions Architect or Azure AI Engineer
Benefits & conditions
Alaska Remote $75 - $80 an hour - Full-time, Pulled from the full job description
- Referral program
- Pet insurance
- 401(k)
- Health insurance
- Vision insurance
- Health savings account
- Dental insurance, PAY RANGE AND BENEFITS: Pay Range*: $75-$80/hour *Pay range offered to a successful candidate will be based on several factors, including the candidate's education, work experience, work location, specific job duties, certifications, etc.
Benefits: OpenKyber offers benefits( based on eligibility) that include the following: Medical & pharmacy coverage, Dental/vision insurance, 401(k), Health saving account (HSA) and Flexible spending account (FSA), Life Insurance, Pet Insurance, Short term and Long term Disability, Accident & Critical illness coverage, Pre-paid legal & ID theft protection, Sick time, and other types of paid leaves (as required by law), Employee Assistance Program (EAP).