Associate Director, RDU IT - MLOps

This role is based in Barcelona, with an on-site commitment of three days a week. Fluency in English is required.

Are you ready to architect and scale secure MLOps and LLMOps capabilities that turn cutting-edge AI into measurable impact for patients and the business? Do you want to own the platform vision that empowers teams to deliver production-grade AI quickly, safely and cost-effectively from Barcelona, Spain?

As Associate Director, RDU IT - MLOps, you will report to the AI Director and lead the strategy, architecture, delivery and governance of our enterprise AI platform and accelerators. You will define the paved road for reusable services, set engineering standards, and embed responsible AI so teams across the organization can deploy agentic and generative AI with confidence.

Your leadership will shape future-ready solutions aligned to enterprise architecture, security and regulatory needs. From day one, you will drive outcomes: faster time-to-value for AI products, stronger operational resilience, and year-on-year improvements in unit economics for large-scale model and LLM workloads., * AI Platform Strategy & Engineering: Own the product vision, target architecture, roadmap and engineering delivery for the RDU AI/ML platform, including reusable frameworks, reference architectures, APIs, templates and self-service “paved road” services. Prioritise capabilities against user needs, enterprise standards and measurable business outcomes, and lead solution design decisions and trade-offs across security, integration, data, performance, scalability, interoperability and cost in alignment with Enterprise Architecture.

• Secure, Reliable and Scalable Platforms: Ensure the platform is secure, resilient, scalable and cost-effective, using infrastructure as code, service-level objectives, monitoring, incident management, capacity planning and disaster recovery to support production-grade machine learning, agentic AI and generative AI workloads.

• Software Excellence, Innovation and DevSecOps: Define, implement and govern engineering standards and reusable delivery patterns across AI workstreams, including secure CI/CD, automated testing, code quality, vulnerability management, release controls, observability and production support. Foster innovation through rapid experimentation, evaluation of emerging technologies, and the responsible adoption of proven approaches, using engineering metrics and assurance reviews to drive adoption and continuous improvement.

• Solution Architecture: Lead solution design discussions, translating business and technical requirements into robust, fit-for-purpose solutions. Own solution decisions and trade-offs across security, integration, data, performance, scalability, interoperability and cost, aligned with Enterprise Architecture.

• Technical Leadership & Stakeholder Engagement: Lead engineering teams and communicate solution decisions, delivery progress, risks and outcomes to stakeholders and senior management, ensuring alignment and timely decision-making.

• MLOps & LLMOps: Own the architecture, development and governance of MLOps and LLMOps pipelines, automating model and LLM workflows and integrating them securely with enterprise data, applications and IT systems.

• FinOps and unit economics: Own the platform’s cost model and financial transparency, tracking cost per inference, GPU utilisation and token economics, and operating showback/chargeback mechanisms to internal consumers to drive year-on-year unit cost reduction.

• Responsible AI, Governance and Compliance: Embed security, privacy, data governance, model risk management and responsible AI controls into platform capabilities and solution lifecycles. Ensure proportionate validation, documentation, human oversight, traceability and auditability in accordance with business needs, GxP requirements and applicable regulatory standards.

• AI-Enabled Ways of Working: Champion the responsible adoption of AI across RDU IT, embedding practical AI capabilities into day-to-day workflows to improve productivity, decision-making, collaboration and service delivery.

• AI-Infused IT Capabilities: Drive the adoption of AI-infused capabilities across IT operations (observability, AIOps, AgentOps, service management) in alignment with EA, IT and EAI, delivering reusable solutions that improve resilience, efficiency and engineering productivity.

• Bachelor’s degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field.

• 8+ years in software, cloud, DevOps or AI/ML engineering.

• 4+ years deploying and maintaining ML models in production.

• Proven experience in technical leadership roles delivering platform capabiltiies.

• Hands-on experience building production models with focus on data science operations including serverless architectures, Kubernetes, Docker/containerization, and model upkeep and maintenance.

• Hands-on experience with infrastructure as code using Terraform and Terragrunt to provision and manage secure, repeatable cloud environments.

• Familiarity with API-based application architecture and API frameworks.

• Experience with CICD orchestration frameworks, such as GitHub Actions, Jenkins or Bitbucket pipelines.

• Deep understanding of software development lifecycle and maintenance.

• Extensive experience with one or more orchestration tools (e.g Airflow, Flyte, Kubeflow).

• Extensive experience in Vector databases, RAG patterns, LangChain/LlamaIndex-class frameworks, evaluation harnesses, and prompt and guardrail management.

• Experience working with MLOps tools like experiment tracking, model registry tools and feature stores (e.g MLFlow, Sagemaker, Azure).

• Strong programming skills in Python and experience with libraries such as Tensorflow, Keras, or PyTorch.

• Proficiency in MLOps best practices, including model training, testing, deployment, and monitoring.

• Experience with cloud computing platforms, such as AWS, Azure or GCP.

• Strong understanding of software engineering best practices and agile methodologies.

• Strong understanding of data structures, algorithms, and machine learning techniques.

• Excellent communication and collaboration skills with the ability to work in a cross-functional team environment

• Ability to work independently and self-driven with strong problem-solving skills., * Experience in the pharmaceutical industry or related fields.

• Master’s degree in a related field or ongoing education towards an advanced degree.

• Strong knowledge of AI techniques and tools, including NLP, optimization, and deep learning.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That’s why we work, on average, a minimum of three days per week from the office. But that doesn’t mean we’re not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.