Lead AI Engineer

About the role

IFS is hiring a Lead AI Engineer in Madrid to design and build applied AI solutions that deliver measurable business value from concept through scalable production deployment. The role involves architecting enterprise AI systems based on 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.
• Build 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.
• Set up evaluation, monitoring and quality practices including testing, benchmarking, observability and continuous improvement.
• Lead technical design discussions, architecture reviews and engineering best practices across teams.
• Mentor engineers and develop reusable AI capabilities and frameworks.
• Work with product teams, architects, domain experts, customers and partners to 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.

Required profile

• Bachelor's degree in computer science, software engineering, AI, data science or a related field; master's degree advantageous.
• 8+ years of professional experience in AI, machine learning and/or software engineering with a proven track record of delivered projects.
• Experience bringing incubated AI solutions to production, including scoping, design, development, testing, deployment and monitoring.
• Experience collaborating with customers, stakeholders and domain experts, and prototyping quickly in real-world environments.
• Strong communication skills and comfort in ambiguous, fast-moving environments.

Required skills

• Programming in Python, Go, C# or TypeScript.
• Backend fundamentals: APIs, distributed services, cloud-native architectures, CI/CD, integration, automation and security.
• DevOps and MLOps/LLMOps, including infrastructure-as-code tools such as Terraform and Helm Charts.
• Context engineering: retrieval architecture, embeddings, vector databases and search technologies.
• Desirable: LLM serving platforms (vLLM, LiteLLM, KServe), foundation models (OpenAI, Anthropic, Llama, Mistral, Cohere), agentic frameworks (LangGraph, CrewAI, AutoGen), evaluation tools (MLFlow, Ragas, Langfuse), Kubernetes, Docker, GPU infrastructure, Hugging Face ecosystem, and cloud AI services (Azure, AWS, Google Cloud).