SPOTIO
Internet
MachineLearning/AIEngineer
“Machine Learning / AI Engineer at SPOTIO. Skills: ML Engineering, MLOps, Python, Gradient Boosting. Own ML lifecycle. Operate production scoring service”
What You'll Achieve.
Complete working grasp of ML and data pipeline; Confidence monitoring production models; Ability to retrain and promote new model versions; Working knowledge of underlying data model; Informed point of view on improvements
Industry & Context.
Resolve scoring failures; Resolve batch issues; Triage production failures; Diagnose scoring failures
On-call responsibility for ML-related incidents
What They're Looking For.
Must Have
At least 5 years of professional experience in ML engineering, applied data science, or a closely related role, Python skills, with experience building and deploying production ML services (FastAPI or equivalent), Hands-on experience training, evaluating, and deploying gradient boosting models (LightGBM, XGBoost, or similar) in production, Experience with model explainability tools, particularly SHAP, Infrastructure-as-code experience, ideally with Terraform, Working knowledge of MLOps practices: model registries, versioned artifacts, drift monitoring, and automated retraining pipelines, Proficiency in SQL with an understanding of query patterns, connection pooling, and performance considerations in high-volume environments, Experience with PyTorch, scikit-learn, NumPy, and Pandas (or Polars), Precision, proactivity, and comfort taking full ownership of systems, Proficiency in English, Education related to computer science, mathematics, statistics, or a related field
Nice to Have
Experience with multi-armed bandits or reinforcement learning approaches, Familiarity with Elasticsearch, Azure Event Hubs, Stream Analytics, or ADLS Gen2, Production experience with a major cloud platform (Azure, AWS, or GCP), Experience with Kubernetes in a production environment, Azure DevOps Pipelines or Argo CD, Exposure to sales CRM data, territory management, or field sales workflows
What You'll Do.
Operate production scoring service
Manage model retraining cycle
Triage production scoring failures
Manage ML feedback loops
Integrate ML outputs into LLM layer
Design new AI features
Contribute to SDLC ceremonies
How You'll Work.
Team & Collaboration
Collaborate with engineering teams; Collaborate with product teams; Collaborate with product and engineering leadership; Collaborate with Gdansk engineering team; Collaborate with QA team
Communication Scope
Proficiency in English
Process & Methodology
Agile planning
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