🛠 Tech Stack

💼 About This Role

You'll build and own the ML infrastructure powering Later's AI capabilities, from experimentation to production. Your work will accelerate data science initiatives and enable scalable model deployment across AWS and GCP. This role defines the standard for ML Ops at a company trusted by Nike and Unilever.

🎯 What You'll Do

• Design and maintain production-grade model deployment and inference systems.
• Automate end-to-end ML lifecycle workflows including training pipelines and rollback strategies.
• Implement monitoring systems for model performance, latency, drift detection, and infrastructure health.
• Operate across AWS and GCP to manage GPU-based training and inference workloads.

📋 Requirements

• 4+ years of experience in ML Ops or ML infrastructure roles.
• Hands-on experience with Amazon SageMaker, Docker, and Flask-based APIs.
• Strong programming experience in Python.
• Experience with infrastructure-as-code tools such as Terraform or CloudFormation.

✨ Nice to Have

• Experience supporting LLMs or generative AI pipelines.
• Familiarity with feature stores (e.g., Feast) or real-time inference systems.
• Knowledge of model-serving frameworks like TorchServe or Seldon.

🎁 Benefits & Perks

• 🏖️ Flexible PTO
• 💰 Equity
• 🏥 Health, dental, and vision insurance
• 📈 401(k) matching
• 💻 Remote-friendly culture

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Phone Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Hiring Manager Interview· 45 min