Description

You will design and manage the infrastructure required for machine learning model lifecycles.

Responsibilities

• Build and maintain secure, scalable infrastructure for ML model training, testing, and deployment using open-source tools.
• Create reusable deployment templates to standardize production paths across teams.
• Translate prototype models into resilient, monitored, and observable production systems.
• Implement guardrails and controls to ensure compliance with internal standards like SR 11-7 and ISO 42001.
• Establish CI/CD pipelines with hooks for testing, scanning, and validation of model code and artifacts.

Required Skills

• 6+ years of experience in software, data, or ML engineering roles.
• Hands-on experience with orchestration frameworks such as MLflow, Metaflow, or Airflow.
• Production experience with Kubernetes, Docker, and Helm.
• Deep understanding of Python and software engineering best practices.
• Experience implementing CI/CD pipelines and infrastructure-as-code in cloud or hybrid environments.
• ML Engineering expertise.

Preferred Skills

• Experience with model monitoring, drift detection, or lineage tools such as Evidently AI, Feast, or LakeFS.
• Experience serving models using KServe, Ray Serve, or Triton Inference Server.
• Familiarity with security tools like Trivy, Aqua, or Snyk.
• Exposure to LLM/RAG architecture or GenAI platform integration.

Key Skills

Education

Any Gradute