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About This Role
We are One Team. We make it happen. We areUnstoppable.
Babel, enriching people through technology
BABEL es una consultora tecnológica multinacional especializada en aplicar sus servicios y conocimiento tecnológico en los procesos de aceleración digital de sus clientes, grandes empresas y organismos públicos.
¿Cuál es nuestro plan estratégico?
¡Miramos hacia el futuro! Nuestro plan estratégico Hiperespacio 2029 promete ser un viaje emocionante, lleno de oportunidades para crecer y desarrollarse profesionalmente. Alcanzar 1000 millones de facturación, un reto que estamos seguros de que con la colaboración y el talento de nuestra gente, será otra historia de éxito que escribiremos juntos y juntas.
¿Qué buscamos?
Objetivo del Rol
Buscamos un Data & AI Engineer Junior para apoyar en el desarrollo, implementación y mantenimiento de soluciones de datos, analítica e inteligencia artificial.
La persona seleccionada participará en proyectos de extracción, transformación, procesamiento y análisis de información, aplicando conocimientos de programación y bases de datos.
Este rol ofrece la oportunidad de fortalecer habilidades técnicas, adquirir experiencia práctica con tecnologías modernas de Data & AI y colaborar con profesionales especializados en un entorno de aprendizaje continuo.
Responsabilidades Principales
- Apoyar en la extracción, transformación, integración y validación de datos provenientes de distintas fuentes.
- Desarrollar y ejecutar consultas SQL para la extracción y análisis de información.
- Crear y mantener scripts básicos en Python para automatizar tareas y procesar datos.
- Participar en la construcción, ejecución y mantenimiento de procesos de carga de datos (ETL/ELT).
- Ejecutar controles y validaciones para garantizar la calidad, integridad y consistencia de la información.
- Apoyar en el desarrollo y mantenimiento de soluciones de Data Engineering, Analytics e Inteligencia Artificial.
- Realizar pruebas funcionales básicas y documentar los resultados obtenidos.
- Elaborar y mantener documentación técnica de procesos, scripts y desarrollos.
- Colaborar con equipos técnicos en la identificación y resolución de incidencias.
- Participar en iniciativas de mejora continua y adopción de nuevas tecnologías, bajo el acompañamiento de profesionales con mayor experiencia.
Formación Académica
Bachillerato universitario concluido en Ingeniería en Sistemas, Ingeniería Informática, Ingeniería en Computación, Ciencia de Datos o afines.
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Experiencia Requerida
- Al menos 1 año de experiencia en desarrollo de software, análisis de datos, bases de datos o funciones relacionadas.
- Experiencia básica utilizando SQL y Python en proyectos académicos, profesionales o personales.
- Conocimientos fundamentales de programación y manejo de bases de datos.
Conocimientos Técnicos Indispensables
- SQL: consultas, filtros, joins y operaciones básicas sobre datos.
- Python: fundamentos de programación, estructuras de datos y desarrollo de scripts.
- Conocimientos básicos de bases de datos relacionales.
- Fundamentos de procesamiento y transformación de datos.
- Manejo básico de Git o herramientas de control de versiones.
Conocimientos Deseables
- Plataformas Cloud: Microsoft Azure, AWS o Google Cloud.
- Microsoft Fabric.
- Power BI y herramientas de visualización de datos.
- Databricks.
- Apache Spark y PySpark.
- Integración y consumo de APIs REST.
- Conceptos básicos de Data Lakes, Data Warehouses y ETL/ELT.
- Fundamentos de Machine Learning e Inteligencia Artificial Generativa (GenAI).
- Uso de herramientas de IA aplicadas al desarrollo, como GitHub Copilot.
Competencias Clave
- Pensamiento analítico: capacidad para comprender información, identificar problemas y proponer soluciones.
- Aprendizaje continuo: interés por desarrollar nuevas habilidades técnicas y mantenerse actualizado.
- Resolución de problemas: capacidad para analizar incidencias y buscar soluciones de manera estructurada.
- Trabajo en equipo: disposición para colaborar con profesionales de distintas áreas.
- Comunicación efectiva: habilidad para comunicar avances, dudas y resultados de forma clara.
- Atención al detalle: enfoque en la calidad y precisión de los datos y desarrollos.
- Proactividad: iniciativa para aprender, investigar y contribuir a la mejora continua.
- Organización: capacidad para gestionar tareas y cumplir con los compromisos establecidos.
#babel
¿Qué ofrecemos?
Babel, the great way to achieve the success.
¿Quieres formar parte de un equipo en expansión, comprometido e innovador que hace historia cada día? En Babel te acompañamos en tu camino hacia el éxito. Creemos en el talento de las personas y lo queremos potenciar.
Trabajar en Babel es mucho más que trabajar en una empresa, es unirse a un equipo de personas con una misión compartida y a un modelo de compañía centrado en valores.
Y lo hacemos promoviendo la diversidad, la equidad y la inclusión, garantizando la igualdad de oportunidades a todas las personas candidatas, en un entorno libre de cualquier tipo de discriminación.
Además,
- Foco en trabajar con últimas tecnologías y proyectos innovadores, pero sobre todo, acompañamos a la transformación digital y acercamos la tecnología a la sociedad.
- Comunicación y transparencia: Siempre estarás informado de las últimas noticias de la compañía.
- Contribuimos a la formación constante de nuestros Babelievers.
- Programa de promociones profesionales y desarrollo de talento.
- Trabajo con proyectos de diversos países lo que nos permite aprender de diferentes culturas.
- Contribuimos en tu salud física y emocional (Charlas, competencias deportivas).
- Participamos del crecimiento de la compañía, reconocemos la participación de cada persona con nuestro bono solidario.
- Apostamos por la flexibilidad laboral – personal.
- Bono por referidos.
- Convenios comerciales según sede.
¿Aceptas el desafío? ¡Te esperamos!
En cumplimiento de la normativa vigente en materia de protección de datos te informamos que el responsable de tus datos personales es GRUPO BABEL (consultar empresas en la política de privacidad), y los utilizará para gestionar tus procesos de selección, tanto actuales como futuros y el resto de finalidades descritas en la web. Este tratamiento está basado en el consentimiento del candidato. Los datos personales recabados no serán en ningún caso cedidos a terceros salvo por obligaciones legales expresas. Puedes ejercer los derechos que te asisten sobre protección de datos en la dirección [email protected]*. Toda la información sobre política de privacidad en nuestra web.*
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 Babel, 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. Entry-level AI roles across all categories have a median of $111,000.
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.
Babel AI Hiring
Babel has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Jose, CA, US.
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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