Machine learning Architect

Principal AI/ML Architect & Applied AI LeadOver 20 years of market experience, Intellias brings together technologists, creators, and innovators across Europe, North and Latin America, and the Middle East. Join our international team and help solve the advanced technology challenges of tomorrow.Project OverviewWe are seeking a highly experienced and hands-on Principal AI/ML Architect & Applied AI Lead to drive the design, development, and operationalization of enterprise-scale AI systems across both research and production environments.This role combines deep expertise in Machine Learning, Generative AI, distributed data systems, and cloud-native architectures with strategic leadership capabilities. The ideal candidate will lead complex AI initiatives end-to-end — from experimentation and research to scalable deployment in global enterprise environments.The position requires a strong balance of:Technical leadershipHands-on implementationAI strategy and innovationCross-functional collaborationMentorship and team developmentRequirementsEducation & ExperienceMaster’s or Ph.D. in Computer Science, Data Science, Machine Learning, or a related field10+ years of experience in AI/ML, data science, distributed systems engineering, or related domainsProven experience designing and deploying production-grade AI solutions at enterprise scaleStrong background in both research-driven and industrial AI environmentsExperience leading global or distributed technical teamsDemonstrated success delivering enterprise AI transformation initiativesTechnical ExpertiseAI / Machine LearningLarge Language Models (LLMs)Generative AI systemsNLP / NLU technologiesRetrieval-Augmented Generation (RAG)Agentic AI workflows and orchestration frameworksAI governance and Responsible AI practicesMLOps frameworks and operational AI systemsData Engineering & Distributed SystemsApache SparkDatabricksDelta LakeSQL and NoSQL databasesDistributed computing architecturesStreaming and batch data pipelinesCloud & InfrastructureAzure and/or AWSDocker and KubernetesCI/CD pipelinesInfrastructure-as-Code (IaC)Cloud-native platform architectureInfrastructure optimization and cloud cost managementProgrammingPythonScalaAdditional QualificationsExperience building AI platforms serving multiple teams or business unitsExperience operationalizing AI securely in enterprise environmentsStrong communication and stakeholder management skillsAbility to translate complex technical concepts into business valueResponsibilitiesAI Architecture & StrategyLead the design and implementation of AI/ML solutions across multiple business domainsDrive enterprise adoption of LLMs, Generative AI, NLP/NLU, and advanced analytics solutionsDefine AI architecture standards, MLOps best practices, and scalable deployment strategiesEvaluate emerging AI technologies and identify opportunities for innovation and operational impactTranslate research initiatives into production-ready AI solutionsData & Platform EngineeringArchitect scalable distributed data-processing systems for large-scale datasets and real-time pipelinesDesign and optimize cloud-native AI platforms using modern data engineering frameworksLead cloud migration and modernization initiatives from on-premises environments to Azure and/or AWSImplement efficient data pipelines leveraging Spark, Delta Lake, Databricks, Kubernetes, and containerized environmentsEnsure reliability, scalability, observability, security, and cost-efficiency of AI infrastructureGenerative AI & Conversational SystemsDesign and implement enterprise-grade chatbot and conversational AI platformsLead development of RAG pipelines, agentic workflows, and LLM orchestration systemsDefine governance, evaluation, monitoring, and safety strategies for GenAI systemsCollaborate with research teams to operationalize LLM-based applications securely and responsiblyLeadership & CollaborationLead cross-functional teams composed of data scientists, ML engineers, software engineers, and business stakeholdersMentor engineers and researchers on AI/ML best practices, architecture, and software engineering standardsCoordinate global AI initiatives across distributed teams and multiple geographiesCommunicate technical concepts effectively to executive and non-technical audiencesSupport innovation programs and AI adoption strategies across the organizationWhat We OfferOpportunity to shape enterprise AI strategy at global scaleWork on cutting-edge Generative AI and applied ML initiativesInternational and collaborative engineering environmentAccess to modern cloud-native and AI technologiesProfessional growth, leadership exposure, and innovation-driven culture