MLOps Engineer (AI/ML)

Location: Remote (Worldwide)

Company

hirify.global develops AI and machine learning capabilities supported by an internal ML platform.

What you will do

• Design and operate ML pipelines for training, evaluation, and deployment.
• Build and maintain batch and real-time model-serving infrastructure at scale.
• Implement monitoring and alerting for model performance, drift, and data quality.
• Manage experiment tracking, model versioning, artifact storage, reproducibility, auditability, and governance.
• Optimize training workflows for cost and speed, including GPU scheduling and spot instances.
• Collaborate with data scientists to productionize models and feature pipelines, and automate infrastructure with Terraform and CI/CD.

Requirements

• 3–6 years of experience in MLOps, ML engineering, or platform engineering.
• Strong Python skills and experience building production ML pipelines.
• Hands-on experience with Kubernetes and Docker.
• Familiarity with ML frameworks such as PyTorch and TensorFlow, plus model-serving tools.
• Experience with cloud ML services such as AWS SageMaker, GCP Vertex AI, or equivalent.
• Understanding of CI/CD principles applied to ML workflows.
• Solid Linux and networking fundamentals.

Nice to have

• Experience with LLM deployment and inference optimization.
• Background with feature stores such as Feast or Tecton.
• Familiarity with Airflow, Prefect, or Dagster.
• Experience with GPU cluster management and cost optimization.
• Knowledge of ML governance and compliance requirements.

Culture & Benefits

• Remote-first environment with flexible hours and an async-first culture.
• Competitive package with early-stage equity.
• High individual impact and challenging technical work.
• Small teams with minimal bureaucracy.
• Technology and AI positioned more than 12 months ahead of industry standards.

Hiring process

• Application review: 2–3 business days.
• 30-minute intro call followed by a 60–90-minute technical screen.
• Team interview lasting 2–3 hours across multiple sessions, followed by an offer within 1–2 business days.