Senior Infrastructure Engineer - AI/ML

Given our continued growth, we always have room for more intellect, energy, and enthusiasm - join our global team and see why it's so special to be a part of Mitratech!

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  Design, deploy, and maintain scalable and secure infrastructure supporting AI and ML workloads.

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  Build and maintain AWS cloud environments for compute (EC2, ECS/EKS, Lambda), storage (S3, EFS, FSx), and networking (VPC, Transit Gateway, PrivateLink, Route 53, load balancers).

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  Implement security best practices using IAM, KMS, Secrets Manager, GuardDuty, and Security Hub.

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  Support and optimize AI/ML workloads across AWS services (SageMaker, Bedrock, Batch, Step Functions).

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  Develop and maintain Infrastructure as Code (IaC) using Terraform, AWS CDK, and CloudFormation.

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  Manage containerized workloads and orchestration platforms (Docker, EKS, Fargate), including GPU scheduling and scaling.

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  Set up and maintain monitoring and observability frameworks using CloudWatch and OpenTelemetry.

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  Build and manage CI/CD pipelines (CircleCI, GitHub Actions, GitLab CI) for infrastructure automation and ML/Gen AI deployments.

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  Collaborate with ML and Generative AI teams to scale models, optimize performance, and design efficient prompt or inference pipelines.

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  Develop runbooks and SOPs for AI service deployment, troubleshooting, and performance optimization.

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  Ensure security, compliance, and data protection across AI datasets and environments.

• Strong proficiency in Linux administration and systems-level troubleshooting.

• Deep expertise in AWS cloud services, with experience in compute, storage, networking, and security domains.

• Proficiency in container orchestration (Kubernetes/EKS) and infrastructure automation tools.

• Hands-on experience with IaC tools such as Terraform, AWS CDK, or CloudFormation.

• Familiarity with monitoring, logging, and observability stacks (Prometheus, Grafana, OpenTelemetry).

• Experience implementing CI/CD pipelines for automated deployment and testing.

• Understanding of AI/ML concepts, including model deployment, inference scaling, and LLM performance tuning.

• Working knowledge of security best practices, IAM roles, encryption, and compliance controls.

• Excellent collaboration and communication skills to partner with ML engineers, data scientists, and product teams.

• A Master’s degree in Machine Learning, Computer Science with a preference for specialization in the NLP domain.