Senior Director, AI Engineering, Enabling Units

Senior Director AI Engineering Enabling Units

Barcelona Spain

Competitive salary and benefits

Introduction to role

This senior engineering leadership role is accountable for the technical strategy architecture team performance and production delivery of AI solutions across AstraZenecas Enabling Functions Legal Procurement Finance Business Development Audit and Compliance. The focus is on owning and executing the engineering vision not programme management. The Senior Director builds and leads high-performing engineering teams sets technical standards and works closely with EA IT and Data teams internal stakeholders and external partners to scale AI from opportunity identification through to enterprise-wide impact. The role delivers scalable production-grade AI systems in environments where regulatory sensitivity data integrity and operational resilience are essential. How will AI reshape the way a global company contracts spends reports audits and stays compliant This role leads that transformation.

Accountabilities

Lead the multi-year engineering strategy for AI across Enabling Functions aligning architecture team capability and delivery sequencing with business priorities and value. Define and evolve a modular reusable AI platform architecture that supports cross-functional use cases from day one enabling rapid reuse of components such as document intelligence risk scoring and forecasting. Make high-consequence technical decisions on build-versus-buy model strategy (including foundation models fine-tuning and RAG) integration patterns and platform selection always balancing scalability risk and total cost of ownership.

Build and lead a multi-disciplinary engineering organisation spanning software ML data and platform engineering attracting and developing exceptional talent while creating clear career paths for senior technical leaders. Set engineering culture and standards for code quality testing documentation peer review and production readiness so that solutions are robust from prototype through to long-term operation. Shape team topology and investment allocation across discovery delivery and sustainment to ensure capacity is focused on the highest-impact opportunities.

Own end-to-end engineering delivery from architecture through to production deployment scaling monitoring and lifecycle management. Partner with EAIT to leverage enterprise platforms infrastructure and shared services so that Enabling Function solutions are built on common foundations and contribute back to enterprise capability. Establish standards for model serving data pipelines APIs integration patterns security observability and MLOps practices including CI/CD automated testing performance monitoring incident management and capacity planning. Ensure rapid experimentation with clear gates between prototype pilot and production so that innovation moves quickly without compromising quality.

Drive scaling through partnership as a defining feature of the role. Orchestrate delivery across EAIT shared services functional teams in Legal Procurement Finance Audit and Compliance and external technology partners to multiply impact without linear headcount growth. Identify where solutions proven in one domain can be adapted to others for example reusing document intelligence capabilities from Legal in Procurement or Audit and build a scaling model that embeds reuse by design. Shape joint programmes with external partners defining technical scope integration architecture IP boundaries and quality standards to ensure outcomes are production-grade rather than isolated proofs-of-concept.

Own the data engineering strategy for AI across Enabling Functions. Define pipelines feature stores and data products designed for quality governance lineage and reuse across SOX-relevant financial data legally privileged documents and PII subject to GDPR/HIPAA. Drive integration architecture with core enterprise platforms such as ERP systems CLM tools procurement platforms and GRC solutions without creating ungoverned data stores or fragile dependencies. Embed governance-by-design into engineering practices through model registration data/model cards performance monitoring frameworks and human-in-the-loop oversight.

Ensure compliance with EU AI Act requirements as they evolve GDPR HIPAA SOX and legal privilege protections by building responsible AI capabilities at scale including automated fairness testing explainability pipelines and decision audit trails embedded into standard workflows. Own technical risk management across the portfolio covering model degradation data quality issues dependency risk and integration risk. Act as a senior technical partner to C-suite stakeholders across Enabling Functions translating engineering complexity into business language so leaders can make informed investment decisions.

Represent Enabling Functions within the enterprise AI engineering organisation and EAIT leadership forums. Contribute to enterprise governance including architecture review boards and standards evolution. Drive reuse and knowledge sharing by identifying where patterns developed for Enabling Functions can accelerate delivery elsewhere in the company and where enterprise patterns can be applied back into finance legal or compliance contexts. Advocate for the specific needs of regulated process-critical functions within broader platform roadmaps so that enterprise AI capabilities are fit for purpose in these demanding environments.

Essential Skills/Experience

Leadership

Significant experience in engineering roles with some of this experience being at Director level or above leading organisations of 50 engineers

Proven production-scale AI/ML delivery in complex cross-functional environments not just prototypes

Track record of scaling through partnership orchestrating across internal platform teams business functions and external partners

Technical

Deep architectural expertise across the AI/ML stack model development MLOps data engineering cloud infrastructure and integration design

Authoritative judgement on build-vs-buy model strategy (foundation models fine-tuning RAG) and platform selection

Strong software engineering standards CI/CD testing observability production operations

Domain and Governance

Delivery within regulated environments where auditability data sensitivity and compliance are non-negotiable

Practical AI governance experience model validation bias detection explainability monitoring human-in-the-loop design

Working knowledge of GDPR and at least one of SOX EU AI Act or equivalent regulatory frameworks

Stakeholder

Credible C-suite partnership ability to translate engineering complexity into business language and influence investment decisions

Experience balancing competing priorities across multiple business functions with architectural coherence

Education

Bachelors degree in Computer Science Engineering Mathematics or related discipline (or equivalent experience)

Desirable Skills/Experience

Domain

Direct experience delivering AI/technology into Legal Procurement Finance Audit or Compliance

Familiarity with enterprise platforms relevant to Enabling Functions (SAP Oracle Coupa CLM GRC tools)

Understanding of pharmaceutical industry context and AI adoption dynamics in life sciences

Technical

Experience with document intelligence NLP and LLM deployment at enterprise scale (fine-tuning RAG guardrails)

Background in data platform engineering feature stores data products real-time and batch pipelines

Hands-on experience with MLOps platforms (MLflow Kubeflow SageMaker Vertex AI or equivalent)

Leadership

Experience building engineering capability from the ground up recruiting structuring and scaling through rapid growth

Track record in matrixed organisations where delivery depends on influencing beyond direct reporting lines

Success driving AI adoption in risk-averse compliance-driven cultures

Strategic vendor and partner management including co-development technical due diligence and integration governance

Education

Advanced degree (MSc PhD) in Computer Science Machine Learning AI or related field

Relevant cloud AI/ML or enterprise architecture certifications (AWS/Azure/GCP Professional TOGAF)

When we put unexpected teams in the same room we unleash bold thinking with the power to

inspire life-changing -person working gives us the platform we need to connect work at pace and challenge

perceptions. Thats why we work on average a minimum of three days per week from the office. But that

doesnt mean were not flexible. We balance the expectation of being in the office while respecting individual

flexibility. Join us in our unique and ambitious world.

AstraZeneca offers an environment where technology leaders can disrupt an industry while seeing direct impact on patients lives; work spans cutting-edge AI and data platforms through to real-world applications that transform how medicines are discovered developed and delivered. Teams are empowered to experiment with modern tools and architectures in a fast-moving yet disciplined setting from hackathons to large-scale programmes supported by strong investment in digital capabilities. Collaboration with diverse experts across science and technology opens up constant opportunities to learn new domains while deepening technical mastery. Continuous development is central: recognition coaching and feedback underpin a culture where curiosity is encouraged every day and career paths can evolve in many directions as technologies change.

If this role matches your experience and ambition to lead AI engineering at scale in a highly impactful setting please apply now.

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Date Posted

Closing Date

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds with as wide a range of perspectives as possible and harnessing industry-leading skills. We believe that the more inclusive we are the better our work will be. We welcome and consider applications to join our team from all qualified candidates regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment) as well as work authorization and employment eligibility verification requirements.