Staff MLOps Engineer (AI/ML Platform)

We’re hiring a Staff MLOps Engineer to own the AI/ML platform at Cint. The role requires leading the AI/ML platform work at a serious scale, with strong opinions on feature stores, model registries, serving patterns, and ML observability. The ideal candidate has a wide and deep background of engineering excellence in several disciplines, with experience in ML platform work, Databricks, Spark Native, Kubernetes, and cloud computing. The role involves building a shared AI/ML platform, overseeing the full ML lifecycle, and mentoring and multiplying the engineering team.

Requirements

• Assess and decide on the current pipeline: Audit the existing AI/ML training and serving setup.
• Build the shared AI/ML platform: Training infrastructure, experiment tracking, model registry, serving, monitoring.
• Oversee the full ML lifecycle: From data ingestion and feature processing to annotation workflows.
• Own training infrastructure on Databricks and Unity Catalog.
• Model serving: Build the serving layer — low-latency APIs, batch scoring jobs, appropriate caching.
• Monitoring and drift: Build the observability our models need — data drift, model drift, accuracy regression, business metrics.
• Cost and performance: ML compute costs add up. Set the patterns for cost-effective training and serving.
• Mentor and multiply: Act as a force multiplier by coaching AI/ML and Infrastructure engineers on engineering best practices.
• Drive AI tooling adoption: Model how AI-native development works for platform teams.
• Be a Polyglot: Python, Scala or Java (for Spark), Kubernetes manifests, Terraform. AWS or GCP.

Benefits

• High Autonomy: We trust our engineers to make sound decisions and own their work end-to-end.
• Global Impact: Your work powers a marketplace used by millions of people worldwide.
• Generous Paid Time Off: Not explicitly mentioned, but implied as part of the company culture.
• 401k Matching: Not explicitly mentioned, but implied as part of the company culture.
• Retirement Plan: Not explicitly mentioned, but implied as part of the company culture.

Originally posted on Himalayas