Senior AI Solution Architect
Fully Remote/ European Residence requiredFull-TimeSenior
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Languages
- English
- Experience
- 10+ years
- Required Skills
- AWSDockerPythonGCPJavaKubernetesMachine LearningC#AzureMicroservicesDistributed Systems
Requirements
- 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.
Responsibilities
- 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.
View Full Description & ApplyYou'll be redirected to the employer's site
