Description

You will design, develop, and maintain a scalable AI platform for investment management technology.

Responsibilities

• Implement and manage Kubernetes clusters to deploy AI models.
• Build platform abstractions to manage cloud-native infrastructure across AWS, GCP, or Azure.
• Maintain automated pipelines for continuous training, testing, and deployment of machine learning models.
• Translate business requirements into scalable technical designs and ensure platform security and compliance.
• Troubleshoot platform performance and reliability issues using standard software engineering practices.

Required Skills

• 10+ years of experience in software and platform engineering.
• Proficiency in Kubernetes, including Helm charts, Kustomize, and custom resource definitions (CRDs).
• Hands-on experience with AWS, GCP, or Azure.
• Expertise in containerization technologies such as Docker and containerd.
• Experience with CI/CD tools including Jenkins, GitHub Actions, and ArgoCD.
• Proficiency in production-grade programming languages including Rust and C++.
• Strong knowledge of Python and data science tools like PyTorch, Jax, and Numpy.
• Experience with Infrastructure as Code (IaC) using Terraform or CloudFormation.
• Proficiency in designing and building scalable APIs and microservices.

Preferred Skills

• Understanding of GPU orchestration and optimization in Kubernetes.
• Experience with distributed data processing frameworks like Spark or Dask.
• Familiarity with MLOps, ML model lifecycle pipelines, and AI model fine-tuning.
• Knowledge of Kafka, event-driven architecture, and NoSQL datastores like Cassandra.

Key Skills

Education

Any Gradute