Descripción de la oferta
Senior Machine Learning Operations Engineer
Location: Remote from Spain (an indefinite Spanish employment contract)Make retail great again through the power of technology! Intellias helps retailers provide consistent and customer-centric shopping experiences across all channels with disruptive retail tech solutions. Get on board and make your own contribution to the industry!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.
Requirements:
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
Proven CI/CD design for GenAI/ML (evaluation gates, versioning, canary, rollback) and collaboration with security/governance stakeholders
Sound judgement selecting RAG/vector and provider stacks based on performance, cost, compliance, and portability
Agent orchestration frameworks (e.g., LangGraph/Semantic Kernel) and tooling protocols (e.g., MCP)
Experience operationalizing multi-agent systems (tools/routing/memory/guardrails, human-in-the-loop)
Process automation and enterprise integrations
Excellent communication and interpersonal skills to collaborate effectively with cross-functional teams, stakeholders' leadership
At least Upper-intermediate level of English
Nice to have:
Master or higher degree in Computer Science, Engineering, or related field
On-prem LLM deployments; performance and cost tuning with caching and model routing
AI safety, policy, and compliance experience in sensitive environments
Public speaking and enablement and building reusable accelerators
Domain exposure in automotive, retail, manufacturing, healthcare, energy, finance, or telecom
Responsibilities:
Lead discovery with stakeholders and define adoption roadmaps and reference architectures
Set lifecycle practices for GenAI (LLMOps)
Architect retrieval and provider layers (RAG, vector stores, model gateways) with portability, cost, and compliance in mind
Implement RAG/agent workflows that orchestrate tool-calling, retrieval, and grounded answering
Enable agentic applications at platform level and define solution patterns and evaluation gates (standardized tools, routing, shared memory, HIL, safe fallbacks) aligned with enterprise integration, security, and cost
Set standards for ingestion, chunking, embedding, and indexing pipelines; select and tune vector databases for retrieval
Establish CI/CD, Infrastructure-as-Code, observability, and automated testing
Define governance and safety guardrails
Establish environment strategy and promotion paths, and a clear handover plan to client teams
Package reusable patterns/accelerators, mentor engineers, and support presales and proposals.
