Descripción de la oferta
AI Consultant 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. 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 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: 1. Architecture 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. • Identify architectural risks or gaps and propose mitigation strategies. 2. Compliance & Standards Validation • Verify that all AI development activities follow JTI’s internal development standards, documentation rules, and operational guidelines. • Ensure compliance with model governance, lifecycle management, versioning, and traceability requirements. • Check adherence to security, privacy, and data handling policies. 3. 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. 4. Pre‑Deployment Validation • Conduct formal readiness reviews before solutions are promoted to production. • Provide clear recommendations for required fixes, improvements, or optimization. • Approve or block deployment based on technical quality and compliance. 5. Documentation & Reporting • Produce detailed review reports summarizing findings, gaps, and actionable guidance. • Maintain traceability of assessments across multiple projects throughout 2026. 6. Cross‑Team Collaboration • Collaborate with engineering, data science, architecture, and product teams to clarify requirements and ensure alignment. • Participate in technical workshops and solution walkthroughs. 7. Advisory & Best Practices Enablement • Advise teams on AI/ML and LLM best practices, including architecture, operations, MLOps, evaluation, and productionization. • Help standardize review processes and improve internal frameworks when needed.
