Senior Director, AI Engineering, Barcelona
Empresa
AstraZeneca
Provincia
Barcelona
Ciudad
Barcelona
Tipo de Contrato
Tiempo Completo
Descripción
Senior Director, AI Engineering
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 medicines. In-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.
MLOps, fine tuning, RAG
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 medicines. In-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.
MLOps, fine tuning, RAG