Janeasystems~1mo ago

Senior AI Solution Architect

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Data ScienceOtherSolution ArchitectAi Solution Architect

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Quick Summary

Overview

Janea Systems (USA) is a dynamic team of the best & brightest software engineering specialists and solutions innovators, from around the world. From kernel to cloud, we provide high-impact software development services to Fortune 500 companies.

Key Responsibilities

Architecture & System Design 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.

Technical Tools

awsazurecsharpdockergcpjavakubernetespythonab-testingdeep-learningdistributed-systemsetlmachine-learningmicroservicessystem-design

Fully Remote/ European Residence required OR LATAM Residence

What We Offer

~1 min read

Salary

✓Competitive compensation with benefits, paid vacation, and sick leave.

✓The opportunity to work with a globally diverse team of top engineering talent on the industry’s toughest engineering challenges.

✓Ultra-flexible working conditions – we provide a generous office equipment allowance so you can work from home, we can also provide you with a desk at an office/coworking facility near you, or use both. No business travel necessary.

✓An enjoyable, start-up work environment, with excellent opportunities for professional growth and development.

✓Flexible working hours – as a remote-first company, our focus has always been on getting the job done well, not when or where it gets done.

Full time/ Flexible working hours

Head of Engineering

Engineering Team

Responsibilities

~1 min read

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.

Requirements

~1 min read

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.