Principal MLOps Engineer - GenAI PlatformsLocation: Remote from Spain (an indefinite Spanish employment contract)
La siguiente información ofrece un resumen de las habilidades, cualidades y cualificaciones necesarias para este puesto.
We are looking for a seasoned MLOps Engineer with strong technology and consulting experience to join the AI/ML Practice team at Intellias. Join us to blueprint GenAI platforms and establish LLMOps practices for client teams across industries, partnering from presales and discovery through PoC to production.
Requirements:7+ years in MLOps/platform architecture or adjacent roles, with shipped AI systemsProficient Python and strong software engineering principlesDeep 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 deploymentProven CI/CD design for GenAI/ML (evaluation gates, versioning, canary, rollback) and collaboration with security/governance stakeholdersSound judgement selecting RAG/vector and provider stacks based on performance, cost, compliance, and portabilityAgent 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 integrationsExcellent communication and interpersonal skills to collaborate effectively with cross-functional teams, stakeholders' leadershipUpper-intermediate level of English
Nice to have:Master or higher degree in Computer Science, Engineering, or related fieldOn-prem LLM deployments; performance and cost tuning with caching and model routingAI safety, policy, and compliance experience in sensitive environmentsPublic speaking and enablement and building reusable acceleratorsDomain exposure in automotive, retail, manufacturing, healthcare, energy, finance, or telecom
Responsibilities:
Lead discovery with stakeholders and define adoption roadmaps and reference architecturesSet lifecycle practices for GenAI (LLMOps)Architect retrieval and provider layers (RAG, vector stores, model gateways) with portability, cost, and compliance in mindImplement RAG/agent workflows that orchestrate tool-calling, retrieval, and grounded answeringEnable 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 costSet standards for ingestion, chunking, embedding, and indexing pipelines; select and tune vector databases for retrievalEstablish CI/CD, Infrastructure-as-Code, observability, and automated testingDefine governance and safety guardrailsEstablish environment strategy and promotion paths, and a clear handover plan to client teamsPackage reusable patterns/accelerators, mentor engineers, and support presales and proposals
At Intellias, where technology takes center stage, people always come before processes. xiphteb We're dedicated to cultivating a tech-savvy environment that empowers individuals to unlock their true potential and achieve extraordinary results. Our customized benefits not only prioritize your well-being but also charge your professional growth, making this opportunity an ideal match for tech enthusiasts like you.