IFS

Lead AI Engineer

Madrid, Madrid, Spain · Remote (restrictions not stated) · Full-time

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EmployerIFS
LocationMadrid, Madrid, Spain
Work styleRemote (restrictions not stated)
Employment typeFull-time
Employer posted1 month ago

As a Lead AI Engineer, you will design and build applied AI solutions that drive measurable business value from concept through scalable production deployment. You'll architect enterprise AI systems leveraging large language models, retrieval-augmented generation, and agentic workflows while leading technical strategy and mentoring engineering teams.

Key Responsibilities

Design and architect AI-powered systems using LLMs, RAG, agentic workflows, and orchestration patterns integrated with enterprise data and business processes
Develop secure, maintainable, production-ready software platforms and cloud-native services that orchestrate models, tools, retrieval systems, and enterprise workflows
Build rapid prototypes and proof-of-concepts to validate new technologies and identify business opportunities
Establish comprehensive evaluation, monitoring, and quality practices including testing, benchmarking, observability, and continuous improvement
Lead technical design discussions, architecture reviews, and drive engineering best practices across teams
Mentor engineers and develop reusable AI capabilities and frameworks that accelerate delivery across the organization
Collaborate with product teams, architects, domain experts, customers, and partners to identify opportunities and deliver business impact
Influence IFS's AI strategy and long-term technology direction through hands-on delivery, experimentation, and customer engagement, including external-facing innovation through industry events and partner collaboration

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Core Requirements

Bachelor’s degree in computer science, Software Engineering, AI, Data Science, or a related field. Master's degree is advantageous.

8+ years of professional experience in AI, Machine Learning, and/or Software Engineering, backed by a proven track record of successfully delivered projects.

Experience bringing incubated AI solutions to production, including scoping, design, development, testing, deployment, and vigilant monitoring.

Strong programming skills one or more mainstream programming languages such as Python, Golang, C# or TypeScript.

Experience with context engineering, including retrieval architecture, embeddings, vector databases, search technologies, and retrieval optimization techniques.

Strong backend engineering fundamentals, including APIs, distributed services, cloud-native architectures, CI/CD, integration, automation, and security.

A solid background in DevOps and MLOps/LLMOps practices, and familiarity with tools to manage infrastructure as code, like Terraform and package managers like Helm Charts.

Ability to design solutions that integrate enterprise applications, business processes, workflows, and data platforms.

Applied AI & Architecture

Experience designing and implementing AI-driven architectures using LLMs, retrieval-augmented generation (RAG), agentic workflows, orchestration patterns, and enterprise data sources.

Strong understanding of the AI system lifecycle, including evaluation, deployment, monitoring, governance, and continuous improvement.

Experience with MLOps lifecycles, deployment pipelines, model operations, and observability for AI systems is advantageous.

Collaboration & Execution

Experience working closely with customers, stakeholders, and domain experts to define and deliver solutions.

Demonstrated ability to rapidly prototype, experiment, measure outcomes, and iterate quickly in real-world customer and enterprise environments.

Strong communication skills, with the ability to explain complex technical concepts clearly to technical and non-technical audiences.

Comfortable operating in ambiguous, fast-moving environments, translating complex business problems into clear technical strategies, execution plans, and measurable outcomes.

Experience leading technical discussions, influencing architectural direction, mentoring engineers, and driving alignment across teams.

Track record of influencing technical direction and technology strategy through hands-on delivery, experimentation, and evidence-based recommendations.

Experience with two or more of the following technologies is highly desirable

LLM Serving & AI Platforms

vLLM, LiteLLM, KServe or similar LLM serving platforms.

AI gateways, model routing, inference serving and multi-modal orchestration.

Foundation Model

Cohere, OpenAI, Anthrophic, Llama, Mistral, DeepSeek or other open-source LLMs.

Agentic AI

LangGraph, PydanticAI, Semantic Kernel, CrewAI, AutoGen or similar agentic AI frameworks.

Tool calling, MCP, workflow orchestration and autonomous agents.

AI Evaluation & Observability

MLFlow, DeepEval, Ragas, Promptfoo, Langfuse or similar evaluation and observability tools.

Cloud & Infrastructure

Kubernetes, Docker, Helm, Terraform and cloud-native deployments platforms.

GPU infrastructure and inference optimization.

Model Development

Hugging Face ecosystem (Transformers, PEFT, LoRA).

Fine-tuning, model evaluation, benchmarking, prompt engineering and model optimization.

Experience building enterprise AI platforms or developer tooling.

Experience working with AMD, NVIDIA, or other AI accelerator technologies.

Experience with enterprise software domains such as ERP, EAM, Service Management, Manufacturing, Supply Chain, or Field Service.

Experience building customer-facing demonstrations, proof-of-concepts, or innovation showcases.

Contributions to open-source AI projects, technical communities, conferences, or publications.

Experience working with Microsoft Azure, AWS, or Google Cloud AI services.

Nice to Have

Experience building enterprise AI platforms or developer tooling.

Experience working with AMD, NVIDIA, or other AI accelerator technologies.

Experience with enterprise software domains such as ERP, EAM, Service Management, Manufacturing, Supply Chain, or Field Service.

Experience building customer-facing demonstrations, proof-of-concepts, or innovation showcases.

Contributions to open-source AI projects, technical communities, conferences, or publications.

Experience working with Microsoft Azure, AWS, or Google Cloud AI services.

We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles, while also valuing inclusive workplace experiences. By fostering a sense of community, we drive innovation, strengthen connections, and nurture belonging. Our commitment ensures you can work in a way that suits you best, while also engaging with colleagues to share ideas and build meaningful relationships.

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