Location: Remote from Spain (an indefinite Spanish employment contract)
Our client is the fastest-growing global manufacturing company. An international corporation with over a hundred years of history, internationally recognized brands and Reduced-Risk Products.Intellias' mission is to support its strategy and efforts in the Digital and e-commerce space (e-commerce and other apps mobile apps, payment gateways, loyalty system, search engine, employee management, identity management, etc.).A newly conceptualized Digital Eco System is comprised of a set of capabilities including an online shop & website, linking online & offline, customization & personalization, engagement & membership, digital product & services main differences.5+ years in MLOps/platform architecture or adjacent roles, with shipped AI systems~ Proficient Python and strong software engineering principles~ Deep experience with at least one major cloud (AWS/Azure/GCP) and platform engineering (containers, Kubernetes, IaC such as Terraform)~ Experience in designing and guiding scalable machine learning pipelines for model training, validation, and deployment~ Sound judgement selecting RAG/vector and provider stacks based on performance, cost, compliance, and portability~ Upper-intermediate level of English
On-prem LLM deployments; performance and cost tuning with caching and model routingArchitecture Review & Production Readiness Assessment
* Evaluate AI/ML and LLM solution architectures to ensure they are scalable, secure, and aligned with enterprise patterns.
* Assess MLOps/LLMOps pipelines, model serving infrastructure, data flows, and integration points.
* Check adherence to security, privacy, and data handling policies.
Technical Quality Assurance
* Perform in‐depth technical code reviews, configuration reviews, and environment checks.
* Validate model performance metrics, evaluation methodology, drift controls, and monitoring strategies.
* Review model explainability, responsible‐AI controls, and risk assessment outputs.
Pre‐Deployment Validation• Approve or block deployment based on technical quality and compliance.Maintain traceability of assessments across multiple projects throughout 2026.Collaborate with engineering, data science, architecture, and product teams to clarify requirements and ensure alignment.• Advise teams on AI/ML and LLM best practices, including architecture, operations, MLOps, evaluation, and productionization.•