Nift is looking for a hands-on ML Ops Engineer to partner with data scientists and turn their models into production-ready systems. The ideal candidate will have experience in ML Ops, software engineering, and cloud and container technology.
Requirements
- 5+ years of experience in ML Ops, including ownership of ML infrastructure for large-scale systems
- Strong coding, debugging, performance analysis, testing, and CI/CD discipline
- Extensive commercial experience with Python developing automated pipelines bringing ML models to production
- Production experience on AWS, DataBricks, Docker + Kubernetes
- IaC: Terraform or CloudFormation for managed, reviewable environments
- ML tooling: MLflow/SageMaker (or similar) with a track record of production ML pipelines
- Monitoring/observability: ML monitoring (quality, drift, performance) and pipeline alerting
- Collaboration: Excellent communication, comfortable working with data scientists, analysts, and engineers in a fast-paced startup
- PySpark/Glue/Dask/Kafka: Experience with large-scale batch/stream processing
- Analytics platforms: Experience integrating 3rd party data
- Model serving patterns: Familiarity with real-time endpoints, batch scoring, and feature stores
- Governance & security: Exposure to model governance/compliance and secure ML operations
Benefits
- Competitive compensation
- Flexible remote work
- Unlimited Responsible PTO