Lead AI Engineer

Location : Hybrid (Madrid)

About Baluarte

Baluarte is defining the future of enterprise AI. We're building the evaluation system that enables production-grade autonomous agents at scale —the critical infrastructure that transforms experimental AI into reliable, deployable intelligence.

We develop comprehensive AI platforms that empower organizations to build, deploy, and rigorously evaluate intelligent systems in real-world environments. Our integrated suite of tools enables teams to orchestrate complex workflows, maintain complete observability, continuously benchmark performance against production standards, and iteratively enhance autonomous systems with confidence. We're establishing the evaluation standards and governance frameworks that will become the backbone of enterprise AI operations.

We champion creativity, execution excellence, and forward-thinking solutions. Our teams are dynamic, collaborative, and results-oriented—driven by the mission of making AI systems that organizations can actually trust and scale.

If you're passionate about shaping how enterprises build and deploy intelligent systems, and want to make a foundational impact on the future of AI in business, we'd love to meet you.

What You'll Do

As a Lead AI Engineer at Baluarte, you'll drive the development and deployment of production-grade AI systems and intelligent agent workflows that define how enterprises evaluate and operate autonomous intelligence at scale. Working within a collaborative squad, you'll architect sophisticated AI solutions that combine reasoning capabilities, knowledge management, and external tool integration—while building the evaluation frameworks and infrastructure that enable these systems to be trusted and deployed in real-world environments. You'll own the complete ML lifecycle, from data preparation through rigorous performance assessment, and ensure every model and agent system meets the production standards that matter.

→ Design and implement robust pipelines for data ingestion, processing, and feature engineering across diverse data sources

→ Deploy machine learning models and large language models to production with comprehensive evaluation frameworks that guarantee performance, stability, and auditability

→ Create efficient fine-tuning workflows for LLMs with version control and systematic hyperparameter management, ensuring measurable improvements

→ Establish evaluation frameworks (comparative testing, LLM-based assessment, automated validation) and monitoring infrastructure that tracks performance metrics, detects model drift, and triggers corrective actions

→ Build and optimize retrieval-augmented generation (RAG) systems with rigorous performance evaluation and production reliability

→ Develop enterprise-grade agent workflows with thorough LLM output assessment and systematic validation

→ Enhance computational efficiency and resource optimization for high-volume inference at scale

→ Collaborate with product and implementation teams to deliver measurable, production-grade solutions that set new standards for enterprise AI

What We're Looking For

We seek someone with solid technical foundations, proven track record of delivery, and expertise in building production-ready AI systems. Here's what excellence in this role entails :

1 / Core Requirements :

✅ 4+ years of professional experience in data engineering, machine learning engineering, or applied AI

✅ Track record of taking models from development to production, with focus on inference optimization

✅ Hands-on expertise with at least one agent framework (LangGraph, LlamaIndex, or similar)

✅ Demonstrated experience fine-tuning and training deep learning models

✅ Advanced Python programming skills and proficiency with at least one deep learning framework (PyTorch, TensorFlow)

✅ Experience architecting data pipelines (batch processing, streaming) using tools such as Airflow, Spark, or Kafka

✅ Strong foundation in machine learning theory (overfitting / underfitting dynamics, classification paradigms, evaluation metrics)

✅ Practical experience with modern cloud infrastructure (AWS, GCP, or Azure)

2 / Preferred Skills :

Production experience with LLMs, prompt engineering, and RAG systems

Working knowledge of vector databases, semantic search, and information retrieval techniques

Understanding of MLOps methodologies : versioning, reproducibility, automated deployment

Experience reducing inference latency and optimizing operational costs

Background in regulated industries or enterprise-scale deployments (fintech, healthcare, insurance)

Who You Are

Strategic Problem-Solver : You identify technical challenges early, develop thoughtful solutions, and execute independently

Committed & Reliable : You take ownership of systems and infrastructure, delivering on your commitments consistently

Innovative Builder : You thrive amid uncertainty, adapt quickly to changing priorities, and excel in dynamic startup environments

Collaborative Leader : You work effectively across teams, welcome feedback, share knowledge generously, and help others grow

What You'll Get

Competitive base salary (€75,000 / yr to €100,000 / yr) + performance-based incentives

Equity participation opportunities for exceptional performers

Comprehensive health and wellness benefits

Flexible hybrid working arrangement + mobility support as needed (based in Madrid)

30 days of annual leave (in addition to local statutory holidays)

Interview Process

We value your time and have streamlined our hiring process for efficiency. Most candidates complete all stages within 2 weeks. Here's what to expect :

Discovery Call – 30 minutes to understand your background, motivation, and alignment with our mission

Practical Assessment – Real-world technical challenge reflecting actual projects at Baluarte

Technical Deep Dive – In-depth discussion of your assessment, technical expertise, and problem-solving approach

Values & Vision Conversation – Exploration of your career goals, professional values, and cultural fit

Final Offer – Conversation with leadership and formal offer