🛠 Tech Stack

💼 About This Role

You'll design and build production-grade ML infrastructure that powers Later's AI capabilities, owning systems for model experimentation, training, deployment, and monitoring. You'll accelerate data science initiatives by establishing best practices and scalable solutions. This role defines ML Ops standards at a leading influencer marketing company.

🎯 What You'll Do

• Design and maintain production-grade model deployment and inference systems
• Automate end-to-end ML lifecycle workflows including training and validation
• Implement robust monitoring for model performance, drift, and infrastructure health
• Operate across AWS and GCP for training and inference workloads

📋 Requirements

• 4+ years in ML Ops, ML infrastructure, or related roles
• Experience with cloud-native environments (AWS and/or GCP) for ML workloads
• Proven track record designing CI/CD pipelines for ML systems
• Strong experience with Amazon SageMaker, Docker, Flask, and infrastructure automation

✨ Nice to Have

• Experience supporting LLMs or generative AI pipelines
• Familiarity with feature stores (e.g., Feast) or real-time inference systems

🎁 Benefits & Perks

• 🏖️ Remote-friendly culture
• 💰 Competitive equity package
• 📈 Learning & development budget
• 🏥 Comprehensive health coverage

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Call· 30 min
2. 2Technical Interview· 60 min
3. 3Team Fit Interview· 45 min