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About This Role
Job Function: Sales Por qué SoftwareOne?:
SoftwareOne es un proveedor global de soluciones de software y tecnología en la nube. Con presencia en más de 70 países y más de 12\.000 profesionales, ayudamos a las organizaciones a optimizar sus inversiones en software, modernizar sus aplicaciones y liberar el valor de la nube, los datos y la IA.
Las personas que forman SoftwareOne, son el núcleo de todo lo que hacemos. Fomentamos la colaboración sin fronteras, el aprendizaje continuo y las oportunidades de crecimiento en un entorno tecnológico en rápida evolución. Ya sea que tu enfoque esté en la tecnología, el éxito del cliente o las operaciones comerciales, tus ideas importan y tus contribuciones marcan la diferencia.
Únete a un equipo global donde podrás desarrollar tus habilidades, trabajar con tecnologías líderes y generar un impacto real para nuestros clientes.
El puesto: Presales Consultant Data \& AI
Modelo: Hibrido \| Puerto Rico
¿Te apasiona diseñar soluciones innovadoras que ayuden a las organizaciones a aprovechar el valor de los datos y la inteligencia artificial?
¿Disfrutas transformar desafíos de negocio en estrategias tecnológicas de alto impacto?
¿Te motiva trabajar con tecnologías de vanguardia como Microsoft Azure, Fabric, AI y Copilot para impulsar la transformación digital?
En SoftwareOne, ayudamos a las organizaciones a acelerar su evolución tecnológica mediante soluciones modernas de datos, analítica e inteligencia artificial. Como PreSales Consultant Data \& AI, desempeñarás un papel clave en la comprensión de necesidades de negocio, el diseño de soluciones innovadoras y el acompañamiento consultivo durante el proceso comercial. Esta posición combina experiencia técnica, visión estratégica y colaboración para generar valor a través de iniciativas de datos e inteligencia artificial alineadas con los objetivos de nuestros clientes.
Lo que harás* Colaborar con equipos comerciales y clientes para comprender necesidades de negocio y definir estrategias basadas en datos e inteligencia artificial.
- Liderar sesiones de discovery, workshops, demostraciones técnicas y Proofs of Concept (PoCs) para identificar desafíos y validar soluciones.
- Diseñar soluciones integrales de Data \& AI utilizando tecnologías como Microsoft Azure, Microsoft Fabric, Azure AI, Azure OpenAI, Copilot y plataformas analíticas.
- Desarrollar propuestas, alcances de trabajo, presentaciones técnicas y recomendaciones alineadas con los objetivos de negocio de los clientes.
- Trabajar de manera colaborativa con equipos técnicos, comerciales y socios estratégicos para garantizar la viabilidad y el éxito de las soluciones propuestas.
Que esperamos de tí:
- Experiencia en roles de Preventa, Consultoría Tecnológica, Arquitectura de Soluciones o posiciones relacionadas con Data \& AI.
- Conocimiento y experiencia en tecnologías Microsoft para datos e inteligencia artificial, incluyendo Azure, Microsoft Fabric, Azure AI, Azure OpenAI, Copilot, así como herramientas de analítica y BI como Power BI, SQL, Data Lakes y Data Warehouses.
- Capacidad para diseñar soluciones y arquitecturas end\-to\-end de datos e inteligencia artificial, considerando aspectos de gobernanza, seguridad y cumplimiento.
- Experiencia liderando workshops, demostraciones técnicas, Proofs of Concept (PoCs) y desarrollando propuestas de solución orientadas a las necesidades del cliente.
- Fuertes habilidades de comunicación consultiva, pensamiento analítico, colaboración y gestión de stakeholders en entornos multiculturales. Certificación Azure Administrator e inglés intermedio\-avanzado son deseables.
¿Por qué unirse a nuestro equipo?:
En SoftwareOne, formarás parte de una organización global que promueve el crecimiento, la innovación y un impacto significativo. Aquí, tu desarrollo y bienestar son fundamentales. Estos cinco beneficios clave reflejan lo que nos distingue:
- Salud y bienestar integrales.
- Aprendizaje continuo y desarrollo profesional.
- Incentivos basados en el rendimiento y participación en acciones para empleados.
- Modelos de trabajo flexibles e híbridos.
- Participación en proyectos globales.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At SoftwareOne, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
SoftwareOne AI Hiring
SoftwareOne has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Guaynabo, PR, US, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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