Senior I ML/CV Engineer

Stack

Responsibilities

• ▹Move predictive models (delay and duration forecasting, congestion forecasting, cost estimation, anomaly detection) from experiment into regular operation
• ▹Build and maintain feature pipelines over the curated data layer, with a documented catalog of model inputs and lineage
• ▹Make training sets reproducible through table versioning and access to historical data states
• ▹Run batch and near-real-time inference as platform jobs wired into existing dependencies and schedules
• ▹Publish models as endpoints for the application and watch latency and cost per call
• ▹Own release discipline: versioning, retraining, rollback, promotion of models across environments
• ▹Provide quality monitoring, drift detection on inputs and outputs, input validation and explainability support

Requirements

• ▹Production-grade Python and PySpark, and confident Spark SQL
• ▹Databricks at development and operations depth: jobs and orchestration, job clusters and cluster policies, portable project bundles, source control integration
• ▹Unity Catalog: grants on tables and models, lineage, environment separation through catalogs
• ▹Working Delta Lake knowledge: table versions and time travel, change data feed, optimization, data layout and feature read performance
• ▹MLflow: experiment tracking, model registry, version promotion, publishing models as endpoints
• ▹Feature storage and reuse, with consistent computation between training and inference
• ▹Hands-on experience operating models in production: monitoring, drift, retraining, incidents, rollback
• ▹Time-series and forecasting methods
• ▹Git, CI/CD, containers, secure handling of secrets

Nice to have

• ▹Built-in platform monitoring for data and model quality
• ▹Production ML on Azure and integration with cloud services
• ▹Understanding of the platform consumption model and cost tuning for recurring jobs
• ▹Geospatial features and route data
• ▹Environments where a model must be explainable to business owners and auditors
• ▹Transportation, logistics or supply chain

About the company

Softeq was founded in 1997 and specialises in new product development and R&D, and also offers early-stage innovation and digital transformation consulting on a global scale.