Senior Data Engineer, ML & AI Platform

Senior Data Engineer, ML & AI PlatformIf you're looking for a stable workplace with a strong reputation, professional growth opportunities, and a friendly team - Stockmann is the place for you!Take data science and AI workloads from prototype to production on Stockmann's AWS data platform - productising the models built by the lead analysts so they run reliably at scale - and build the foundational capabilities for agentic analytics. As a full member of the data engineering team, the role also adds senior capacity to the data platform roadmap and the decommissioning of the legacy data warehouse.Duties and responsibilities:

• Path to production - Define and roll out a standard route from notebook to production - project template, model registry.
• Priority models in production - Move forecasting, customer segmentation and feedback-processing workloads onto the standard path with monitoring, SLAs and named owners.
• Taking ML in-house - Document and take over data science workloads currently built or maintained by external partners.
• Agentic analytics foundation - Deliver the governed foundation and support a first agentic analytics use case end to end.
• Platform roadmap - Take ownership of a share of the data platform roadmap and legacy data warehouse decommissioning work alongside the Lead Data Engineer and Data Engineer.
• Observability & FinOps for ML/AI - Track cost, performance and usage of ML and LLM workloads, and keep them within agreed budgets.
• ML productisation & ML operations - Turn analyst-built models; own deployment, monitoring, drift checks, retraining and incident handling.
• ML & AI runtime on AWS - Build and maintain the runtime for data science and AI workloads (e.g. SageMaker, containers, Step Functions) with IaC, CI/CD and cost control, within the Lead Data Engineer's standards.
• Agentic analytics foundations - Build what AI agents need to use Stockmann data safely: consistent metric and semantic definitions, catalogue metadata and lineage, controlled data and tool access (e.g. MCP), logging, evaluation and guardrails.
• Core data engineering - Contribute to ingestion, transformation and modelling on S3, Glue and Redshift, including the data platform roadmap and the migration of legacy data flows.

Job requirement:

• 5+ years in data engineering, ML engineering or software engineering with a data focus.
• 2+ years deploying and operating ML models in production, including monitoring and retraining.
• Experience working alongside data scientists or analysts, turning prototypes into maintained services.
• Strong plus: building or operating LLM-based or agentic applications in production.
• Bachelor's or master's degree in computer science, engineering, mathematics or a related field, or equivalent practical experience.
• AWS associate- or professional-level certification (e.g. Machine Learning Engineer, Data Engineer, Solutions Architect) preferred.

We offer:

• Opportunity to gain professional experience in an international company.
• Monthly salary 5000 - 5500 EUR Gross (based on experience and competences).
• Great working environment and support in the work process.
• A wide range of employee benefits and bonuses.
• Workplace in Riga, long term stability.

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