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About This Role
¿Te apasiona la inteligencia artificial y quieres liderar el desarrollo de soluciones tecnológicas con impacto real en las empresas?
En Substrate AI , compañía tecnológica española cotizada en BME Growth y especializada en inteligencia artificial, continuamos impulsando nuestro crecimiento y ampliando nuestro equipo de profesionales.
Nuestra actividad se centra en el desarrollo de soluciones innovadoras de inteligencia artificial para empresas, combinando tecnología propia, innovación y proyectos de transformación digital en diferentes sectores.
En este contexto, buscamos incorporar un/a AI Solutions Architect & Lead , un perfil senior con una sólida trayectoria tecnológica, visión de negocio y capacidad para diseñar, desarrollar e implementar soluciones avanzadas de inteligencia artificial generativa.
La persona seleccionada trabajará directamente con el CEO de la compañía , participando activamente en la identificación de nuevas oportunidades de negocio, el desarrollo de proyectos estratégicos y la implantación de soluciones tecnológicas para nuestros clientes.
Buscamos un profesional con iniciativa, autonomía y orientación a resultados, que quiera asumir un papel relevante en una compañía en crecimiento y contribuir directamente a su desarrollo tecnológico y comercial.
¿Cuáles serán tus principales responsabilidades?
Desarrollo de negocio y relación con clientes
- Participar junto al CEO en reuniones con clientes, socios estratégicos y administraciones para identificar nuevas oportunidades de negocio.
- Analizar las necesidades de los clientes y transformarlas en soluciones de inteligencia artificial viables y con impacto medible.
- Elaborar propuestas técnico-económicas, estimaciones de costes, presupuestos y análisis de rentabilidad.
- Participar en procesos de negociación, licitaciones y desarrollo de nuevas oportunidades comerciales.
Arquitectura y desarrollo de soluciones de inteligencia artificial
- Diseñar e implementar soluciones de IA generativa utilizando nuestra plataforma tecnológica propia, Serenity Star.
- Desarrollar arquitecturas basadas en modelos LLM, RAG, agentes inteligentes y herramientas de automatización.
- Participar directamente en el desarrollo de aplicaciones, integraciones y soluciones tecnológicas mediante Python, .NET y/o TypeScript.
- Integrar soluciones con sistemas empresariales, APIs, bases de datos y plataformas cloud.
- Liderar el proceso técnico desde el diseño y prototipado hasta el despliegue y puesta en producción.
- Garantizar la calidad, seguridad, escalabilidad y cumplimiento normativo de las soluciones desarrolladas.
Liderazgo técnico y gestión de proyectos
- Coordinar equipos técnicos internos y proveedores externos en función de las necesidades de cada proyecto.
- Supervisar la planificación, ejecución, calidad y cumplimiento de los objetivos establecidos.
- Actuar como interlocutor técnico de referencia para los clientes.
- Gestionar el seguimiento y la evolución de las soluciones implantadas.
- Garantizar el correcto funcionamiento de los sistemas en producción, incluyendo monitorización, soporte y mejora continua.
¿Qué buscamos?
- Experiencia mínima de 10 años en desarrollo de software, arquitectura de soluciones y proyectos tecnológicos.
- Al menos 2 años de experiencia demostrable en el desarrollo e implantación de soluciones de IA generativa en producción.
- Conocimientos sólidos de LLMs, arquitecturas RAG, agentes de IA, tool calling y evaluación de modelos.
- Experiencia práctica en desarrollo con Python, .NET y/o TypeScript.
- Experiencia trabajando con plataformas cloud como Azure, AWS o Google Cloud.
- Experiencia en desarrollo de negocio tecnológico, análisis de necesidades, elaboración de propuestas y relación directa con clientes.
- Capacidad para diseñar, desarrollar y desplegar soluciones tecnológicas de principio a fin.
- Experiencia en coordinación de proyectos, equipos técnicos y proveedores externos.
- Conocimientos de operación y mantenimiento de sistemas en producción.
- Excelentes habilidades de comunicación, negociación y orientación a resultados.
- Nivel de inglés C1 y español nativo o bilingüe.
- Residencia en Valencia o disponibilidad para trasladarse y realizar desplazamientos profesionales.
Valoraremos especialmente:
- Experiencia previa como CTO, Solutions Architect o fundador/a de una startup tecnológica.
- Trayectoria profesional en consultoría tecnológica, empresas de inteligencia artificial o proveedores cloud.
- Experiencia en proyectos tecnológicos para sectores como salud, industria o administraciones públicas.
- Conocimientos de RGPD, EU AI Act y desarrollo de soluciones en entornos regulados.
- Experiencia con plataformas de orquestación de agentes de inteligencia artificial.
¿Qué ofrecemos?
- Ubicación: Oficinas situadas en el centro de Valencia.
- Modalidad de trabajo: Híbrida.
- Tipo de contrato: Indefinido.
- Jornada laboral: Completa.
- Incorporación: Inmediata.
- Salario: Retribución competitiva, acorde con la experiencia y conocimientos aportados.
- Desarrollo profesional: Oportunidades de crecimiento y evolución dentro de una compañía tecnológica en plena expansión.
- Proyecto profesional: Participación en proyectos innovadores de inteligencia artificial, con colaboración directa con el CEO y autonomía en la toma de decisiones técnicas.
- Herramientas de trabajo: A cceso a los recursos, equipos y herramientas tecnológicas necesarios para el adecuado desempeño de las funciones.
¿Por qué unirte a Substrate AI?
En Substrate AI creemos que el verdadero potencial de la inteligencia artificial está en su capacidad para transformar empresas, optimizar procesos y generar nuevas oportunidades de negocio.
Por ello, buscamos profesionales que quieran participar activamente en esa transformación, aportando conocimiento, experiencia e iniciativa.
Esta posición ofrece la oportunidad de combinar desarrollo tecnológico, visión de negocio y liderazgo de proyectos , trabajando directamente con la dirección de la compañía y participando en iniciativas estratégicas de inteligencia artificial.
Si cuentas con una sólida trayectoria tecnológica, tienes experiencia desarrollando soluciones de IA generativa y buscas un nuevo reto profesional en una compañía innovadora y en crecimiento, queremos conocerte.
¡Inscríbete y forma parte de Substrate AI!
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Substrate AI, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Substrate AI AI Hiring
Substrate AI has 1 open AI role right now. They're hiring across AI/ML Engineer.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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