AI Engineer

Roche Holding AG; Madrid; Hybrid; EUR 70,000 - 110,000; Full time

Benefits offered by this job

On-site/Hybrid position

Job summary

Roche recherche un ingénieur IA expérimenté pour concevoir et livrer des solutions IA robustes au sein de Pharma Technical Operations. Vous dirigerez des architectures, flux de données, et des pipelines ML/GenAI en collaboration avec les métiers et IT.

Responsable de l’intégration, l’évaluation, l’exploitation et la conformité des solutions, vous progresserez dans un environnement cloud/hybride, avec un accent sur l’amélioration continue et la valeur opérationnelle.

Qualifications

• Diplôme Bachelor en informatique, data science, ingénierie, mathématiques ou équivalent.
• Expérience avérée en livraison de logiciels production-grade et solutions IA.
• Maîtrise pratique de Python et bonnes pratiques d'ingénierie logicielle.

Responsibilities

• Assurer la livraison technique de solutions IA de bout en bout.
• Concevoir des architectures AI et des flux de données évolutifs.
• Conduire l’évaluation et la comparaison de modèles et prompts.
• Collaborer avec les métiers, IT, qualité et équipes digitales.

Skills

Python

LLMs / GenAI

CI / CD

Docker

APIs & microservices

Evaluation frameworks

Team leadership

Education

Bachelor's degree en informatique, data science, ingénierie, mathématiques ou domaine connexe

Tools

DeepAgents

LangGraph

LangChain

AWS

Docker

CI/CD

Job description

Chez Roche, vous pouvez être vous-même et être apprécié pour les qualités uniques que vous apportez. Notre culture encourage l'expression personnelle, le dialogue ouvert et les connexions authentiques, où vous êtes valorisé, accepté et respecté pour ce que vous êtes, vous permettant de prospérer tant personnellement que professionnellement. Voici comment nous visons à prévenir, arrêter et guérir les maladies et à garantir à chacun l'accès aux soins de santé aujourd'hui et pour les générations à venir. Rejoignez Roche, où chaque voix compte.

La position

At Roche, we are committed to delivering greater benefits to our patients by applying digital, data, and AI capabilities to real-world operational challenges. The AI Engineer is a senior hands-on technical contributor within the AI Engineering Cluster, responsible for designing, delivering, evaluating, and operating robust AI solutions across Pharma Technical Operations.

This role owns meaningful parts of end-to-end AI delivery, including solution architecture, data pipelines, machine learning and GenAI systems, agentic workflows, AI evaluation harnesses, backend services, user interfaces, and production operations. The role partners with business, IT, quality, and technical experts to translate complex operational challenges into reliable, scalable, compliant, and measurable AI-enabled products.

Role description

The role is a senior AI engineering role for someone who combines strong technical depth with practical delivery ownership. The AI Engineer is expected to independently lead solution delivery, make sound architectural decisions, mentor other engineers, and ensure AI solutions create measurable business value while meeting reliability, scalability, evaluation, agent management, and compliance expectations.

AI Solution Leadership & Delivery

• End-to-end delivery ownership: Own the technical delivery of AI solutions from problem understanding and design through build, deployment, monitoring, evaluation, adoption, and continuous improvement.
• Solution architecture: Design scalable AI application architectures, including data flows, model integration, GenAI orchestration, APIs, user interfaces, agent workflows, and production infrastructure.
• GenAI and agentic AI: Design and implement advanced GenAI solutions, including RAG architectures, multi-step workflows, tool-using agents, LLM orchestration, and evaluation approaches.
• Engineering quality: Apply and help evolve strong engineering practices, including automated testing, code reviews, CI/CD, observability, documentation, and maintainability.
• Production performance: Ensure AI solutions are reliable, secure, monitored, cost-aware, and continuously improved in production or production-like environments.
• Value delivery: Translate business needs into technical solutions and help define success metrics that demonstrate operational and business impact.

Modern AI Engineering Capabilities

• Evaluation framework design: Design and implement data science-driven evaluation frameworks for AI, GenAI, RAG, and agentic AI systems, including offline benchmarks, golden datasets, synthetic test cases, human evaluation workflows, and automated regression testing.
• AI harness development: Own the development of AI engineering harnesses for repeatable experimentation, evaluation, validation, and comparison of models, prompts, retrieval strategies, agent behaviors, and tool integrations.
• Agentic AI solution ownership: Design and deliver production-grade agentic AI systems, including agent orchestration, tool integration, task decomposition, state handling, memory patterns, guardrails, and human escalation paths.
• Agent lifecycle management: Manage deployed agents across their lifecycle, including versioning, testing, release management, monitoring, incident response, continuous improvement, and retirement.
• Performance and risk monitoring: Implement and operate observability for AI systems, including quality metrics, business impact metrics, cost, latency, failure modes, drift, hallucination risk, grounding quality, and operational reliability.
• Model and workflow comparison: Lead structured experiments to compare foundation models, prompts, embeddings, retrieval approaches, orchestration patterns, and agent configurations.

Technical Expertise

• AI engineering depth: Apply strong knowledge of machine learning, predictive modeling, time-series, computer vision and multimodal AI, as well as LLMs, RAG, agentic AI, prompt engineering, model evaluation, and AI application design.
• Software and platform engineering: Build robust Python-based services, APIs, microservices, dashboards, and integrations using modern software engineering practices.
• Cloud and DevOps: Design and deploy solutions using AWS, Docker, CI/CD pipelines, monitoring, and cloud or hybrid deployment patterns.
• Data engineering: Work with SQL, operational data, time-series data, structured and unstructured data, and data pipelines required for AI products.
• Evaluation methods: Use quantitative metrics, qualitative review, human-in-the-loop assessment, statistical comparison, and production feedback loops to make engineering decisions.
• Regulated delivery: Partner with IT and quality stakeholders to ensure solutions are designed with appropriate compliance, validation, security, privacy, data governance, responsible AI, traceability, documentation, and lifecycle controls.

Technical Leadership & Mentoring

• Engineering mentorship: Coach less experienced engineers on AI engineering, software development, evaluation practices, agentic systems, cloud deployment, and production operations.
• Best practices: Contribute to shared standards, reusable patterns, and engineering guidelines for the AI Engineering Cluster.
• Technical problem-solving: Structure ambiguous technical problems, evaluate solution options, and guide teams toward pragmatic, high-impact outcomes.
• Capability building: Help build AI engineering capability across Pharma Technical Operations through knowledge sharing, examples, and hands-on support.

Collaboration, Adoption & AI Advocacy

• Stakeholder partnership: Partner with process experts, business stakeholders, IT, quality, and other digital teams to identify, design, implement, evaluate, and sustain AI-enabled improvements.
• Organizational alignment: Communicate technical concepts clearly to both technical and non-technical audiences, supporting alignment and adoption.
• AI advocacy: Promote effective, responsible, and value-driven use of AI within Pharma Technical Operations.
• Continuous learning: Stay current with advances in AI, GenAI, agentic systems, evaluation science, software engineering, and cloud technologies, and translate relevant innovations into practical Roche use cases.

Qualifications & Competencies

• Bachelor's degree in computer science, data science, engineering, mathematics, or a related field; or equivalent practical experience.
• Demonstrated track record independently delivering production-grade software, AI, machine learning, data, or analytics solutions.
• Strong hands-on Python expertise and solid knowledge of software engineering practices.
• Experience designing and delivering cloud-based or hybrid applications, preferably using AWS.
• Hands-on experience with modern agent frameworks and harnesses such as DeepAgents, LangGraph, LangChain, or equivalent technologies.
• Strong understanding of LLMs, RAG, prompt engineering, AI agents, model integration, tool use, and evaluation approaches.
• Experience with APIs, microservices, CI/CD, Docker, monitoring, automated testing, code review practices, and AI evaluation harnesses.
• Ability to translate evaluation results into engineering decisions, product improvements, and stakeholder recommendations.
• Ability to lead technical delivery, manage ambiguity, and influence cross-functional stakeholders.
• Experience in industrial, manufacturing, pharmaceutical, biotechnology, quality, or regulated environments is strongly preferred.

Our Culture at Roche

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

* This is an on-site/hybrid position and no relocation assistance is provided.

Qui nous sommes

Un avenir plus sain nous pousse à innover. Ensemble, plus de 100 000 employés à travers le monde sont dédiés à faire progresser la science et à garantir à chacun l'accès aux soins de santé aujourd'hui et pour les générations à venir. Nos efforts aboutissent à plus de 26 millions de personnes traitées avec nos médicaments et plus de 30 milliards de tests réalisés avec nos produits de Diagnostique. Nous nous encourageons mutuellement à explorer de nouvelles possibilités, à favoriser la créativité et à conserver nos grandes ambitions, afin de fournir des solutions de santé qui changent des vies et ont un impact mondial.

Construisons ensemble un avenir plus sain.

Roche est un employeur offrant l'équité en matière d'emploi.