ML Ops Engineer - nift

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 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 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

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 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 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

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 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 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