ML Ops Engineer

November 25

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Nift

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Discover your next favorite business with Nift.

11 - 50 employees

💰 $16.5M Series A on 2018-03

📋 Description

• ML platform: Productionize training and inference (batch/real-time), establish CI/CD for models, data/versioning practices, and model governance • Feature & model lifecycle: Centralize feature generation (e.g., feature store patterns), manage model registry/metadata, and streamline deployment workflows • Observability & quality: Implement monitoring for data quality, drift, model performance/latency, and pipeline health with clear alerting and dashboards • Engineering excellence: Refactor research code into reusable components, enforce repo structure, testing, logging, and reproducibility • Cross-functional collaboration: Work with DS/Analytics/Engineers to turn prototypes into production systems, provide mentorship and technical guidance • Roadmap & standards: Drive the technical vision for ML platform capabilities and establish architectural patterns that become team standards

🎯 Requirements

• Experience: 5+ years in ML Ops, including ownership of ML infrastructure for large-scale systems • Software engineering strength: Strong coding, debugging, performance analysis, testing, and CI/CD discipline; reproducible builds. Extensive commercial experience with Python developing automated pipelines bringing ML models to production • Cloud & containers: Production experience on AWS, DataBricks, Docker + Kubernetes (EKS/ECS or equivalent) • 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 • Be mission-oriented: Proactive and self-driven with a strong sense of initiative; takes ownership, goes beyond expectations, and does what's needed to get the job done

🏖️ Benefits

• Competitive compensation, flexible remote work • Unlimited Responsible PTO • Great opportunity to join a growing, cash-flow-positive company while having a direct impact on Nift's revenue, growth, scale, and future success