Senior AI Solution Architect

Fully Remote/ European Residence required OR LATAM Residence

Full time/ Flexible working hours

Head of Engineering

Engineering Team

• Design scalable architectures for machine learning platforms, AI services, and data processing pipelines.
• Define system-level architectures that integrate machine learning models into distributed production systems.
• Ensure high availability, scalability, and performance of ML-powered applications.
  
  

• Architect end-to-end ML pipelines including data ingestion, feature engineering, training workflows, model serving, and monitoring.
• Design ML infrastructure capable of supporting experimentation, training, and large-scale inference.
• Guide teams in implementing modern MLOps practices across projects.
  
  

• Provide architectural leadership to engineering teams implementing ML-enabled systems.
• Establish best practices for system design, model integration, observability, and reliability.
• Conduct architecture reviews and mentor engineers on building scalable ML-driven software systems.
  
  

• Work closely with ML engineers, backend engineers, data engineers, and DevOps teams to design cohesive AI solutions.
• Partner with client stakeholders to translate complex business requirements into scalable technical architectures.
• Help guide technical decisions during project planning and delivery.
  
  

• Stay current with advancements in machine learning infrastructure, distributed systems, and AI engineering.
• Identify opportunities to improve the scalability and efficiency of ML systems across projects.
• Contribute to internal knowledge sharing and architectural standards across Janea’s engineering teams.
   

• 10+ years of experience in software engineering, distributed systems, or backend architecture.
• 5+ years of experience designing systems that incorporate machine learning or data-driven components.
• Strong experience architecting large-scale, production-grade software systems.
• Deep understanding of machine learning system architecture, model deployment patterns, and ML lifecycle management.
• Strong programming experience in Python, Java, C#, or similar languages.
• Experience designing systems in cloud environments (AWS, Azure, or GCP).
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Strong understanding of data pipelines, distributed systems, and microservices architectures.
• Excellent system design, problem-solving, and technical leadership skills.
• Strong written and spoken English communication skills.
• Degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
  
  

• Experience designing ML platforms or internal AI infrastructure.
• Experience working with LLMs, generative AI systems, or deep learning architectures.
• Familiarity with large-scale distributed training environments.
• Experience with feature stores, model monitoring platforms, or ML observability tooling.
• Experience working in client-facing consulting environments designing enterprise-scale systems.