Technical Director Core Ai Engineering Technical Director Core Ai Engineering Madrid, Spain, Md, Es Oct 24, 2026

Beam Suntory, Inc; Madrid; On-site; EUR 150,000 - 190,000; Full time

Job summary

Beam Suntory, Inc. seeks a hands-on Director of Core AI Engineering to set the technical direction for enterprise AI capabilities and collaborate across AI Product Delivery, data, security, and infrastructure.

You will lead a small, capable team and guide architecture choices, prototypes, and validations to create a blueprint for industrialization by delivery teams. The role requires deep experience in enterprise architecture, AI orchestration, retrieval, and secure engineering practices, with

Qualifications

• 12+ years of engineering or technology-delivery leadership in enterprise environments.
• Experience designing and validating enterprise AI and generative AI capabilities.
• Strong enterprise architecture, systems integration, and secure engineering practices.
• Experience leading distributed technical teams and scalable DevOps practices.
• Excellent technical communication and decision-making skills.

Responsibilities

• Set the Core AI engineering direction and reference architectures for enterprise AI capabilities.
• Own the advanced AI systems stack, including agent runtimes, retrieval, and orchestration.
• Lead technical incubation, prototypes, security reviews, and handoffs to delivery teams.
• Validate quality, safety, and technical readiness with measurable evaluation approaches.
• Build and mentor a high-caliber AI/platform team and drive scalable engineering standards.

Skills

Leadership

Enterprise AI

Systems architecture

Cloud platforms

Security practices

DevOps / SRE

Technical communication

Team mentorship

Distributed teams

Tools

Vector databases

LLMOps tooling

RAG retrieval

Knowledge graphs

Embeddings

Job description

What makes this a great opportunity?

This is a hands-on director-level technical leadership role with a clear mission: identify, design, and validate the next enterprise AI capabilities with the greatest potential to create business value.

The Director of Core AI Engineering will set the technical direction and operating model for emerging and complex enterprise AI capabilities, combining accountable technical leadership with direct involvement in architecture, critical engineering decisions, prototypes, enterprise integrations, code and design reviews, and technical validation.

You will lead a small, highly capable team and partner closely with AI Product Delivery, data, security, infrastructure, and application-engineering leaders to prove what is technically possible, resolve key architectural and integration questions, and create the technical blueprint and evidence needed for capabilities to be industrialized by delivery teams.

Role Responsibilities

Set the Core AI engineering direction

• Own the technical exploration roadmap, target architecture, and engineering standards for emerging enterprise AI capabilities.
• Define architectural boundaries, design assumptions, and technical decision criteria in partnership with Product Delivery, data, security, infrastructure, and engineering teams.
• Make pragmatic decisions on architecture, model access and routing, orchestration, retrieval, integrations, and build-versus-buy choices.
• Create reusable reference patterns for AI orchestration, enterprise integrations, evaluation, observability, security, context management, and LLMOps.

Own the advanced AI systems stack

• Architect and validate agent runtimes, including single- and multi-agent orchestration, workflow routing, human-in-the-loop controls, retries, fallbacks, and recovery mechanisms.
• Design and test RAG and enterprise retrieval approaches, including ingestion, embeddings, vector indexing, hybrid retrieval, access controls, reranking, and knowledge-graph approaches.
• Prototype context-engineering, caching, memory, tool-using AI systems, and closed-loop improvement mechanisms.
• Establish viable model-serving and inference designs, including gateways, routing, latency, scalability, cost, performance, and fallback requirements.
• Select and test fit-for-purpose technologies based on measurable technical evidence.

Lead technical incubation and validated handoff

• Lead prioritized use cases through discovery, prototype, enterprise integration, evaluation, security review, and technical handoff.
• Remain hands-on in architecture, design and code reviews, critical prototypes, technical problem solving, and readiness decisions.
• Define reference implementations, interfaces, quality evidence, non-functional requirements, limitations, and acceptance criteria for industrialization.
• Establish clear handoff and knowledge-transfer points with delivery, platform, security, and operations teams.

Validate quality, safety, and technical readiness

• Establish measurable evaluation approaches covering quality, groundedness, retrieval performance, tool reliability, safety, latency, cost, and regression detection.
• Instrument prototypes to capture system behaviour, usage, failures, traces, model and prompt versions, latency, cost, and user feedback.
• Partner with security and data-governance teams to implement appropriate controls for sensitive data, AI security risks, access, and unreliable outputs.
• Balance ambitious experimentation with disciplined engineering practices.

Build the team and operating model

• Lead, mentor, and develop senior AI and platform engineers, setting priorities and technical quality standards.
• Drive architecture reviews and technical decision-making across the AI portfolio.
• Champion technical learning, reference architectures, and standards that enable delivery teams to scale proven capabilities.

Qualifications

Required

• 12+ years of engineering, data, platform, or technology-delivery experience, including significant leadership responsibility in complex enterprise environments.
• Demonstrated experience designing and validating enterprise AI, generative AI, or intelligent-automation capabilities through working implementation and technical handoff.
• Strong experience in enterprise architecture, systems integration, cloud/data/application platforms, and secure engineering practices.
• Practical experience with AI orchestration, evaluation, observability, security, and adoption practices.
• Ability to lead distributed technical teams and establish scalable engineering, DevOps, SRE, and quality practices.
• Strong technical communication and decision-making skills.

Preferred

• Experience creating reusable AI reference architectures and standards.
• Knowledge of vector databases, embeddings, hybrid retrieval, agentic workflows, structured tool calling, context management, model serving, evaluation, and observability.
• Experience in a global consumer-goods organization or similarly complex distributed enterprise.

While relocation, immigration, and/or tax compliance support are not guaranteed, we may offer assistance to successful candidates depending on factors such as role requirements in accordance with company guidelines. At Suntory Global Spirits, people are our number one priority, and we believe our people grow together in diverse and inclusive environments where their unique insights, experiences and backgrounds are valued and respected. Suntory Global Spirits is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, military veteran status and all other characteristics, attributes or choices protected by law. All recruitment and hiring decisions are based on an applicant’s skills and experience.
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