Solutions Architect (AI/SaaS)

Overview

Responsibilities

• Translate high-level business requirements into detailed technical architectures for AI-driven software.
• Conduct technical feasibility studies to evaluate the suitability of specific machine learning models for client use cases.
• Lead the design of data ingestion pipelines and integration layers between SaaS platforms and AI engines.
• Collaborate with security and compliance teams to ensure AI implementations adhere to industry regulations.
• Build proof-of-concept applications to demonstrate the value of proposed architectural designs to stakeholders.
• Maintain detailed documentation of system designs, API specifications, and infrastructure configurations.
• Provide technical oversight to implementation engineers during the deployment phase of a project.

Qualifications

• A bachelor or master degree in computer science, data science, or a related engineering field is required.
• Extensive experience with cloud infrastructure providers such as AWS, Azure, or Google Cloud Platform is essential.
• Professional proficiency in programming languages such as Python or Java for backend and AI integration is mandatory.
• Demonstrated experience in designing and deploying scalable SaaS architectures within an enterprise environment.
• Deep understanding of machine learning frameworks and the lifecycle of model deployment and monitoring.
• Proven ability to lead technical discussions with both executive stakeholders and software engineering teams.

Nice to have

• Advanced certification in cloud architecture or machine learning from a major provider.
• Previous experience working with vector databases and retrieval-augmented generation architectures.
• A history of contributing to open-source AI projects or publishing technical research in the field.

Work environment

• Work is primarily conducted in office settings or home offices with regular video conferencing.
• Collaboration occurs within cross-functional teams including sales, product management, and engineering.
• The pace of work is influenced by project milestones and client delivery deadlines.
• Standard professional hours are typical, though occasional late hours may occur during critical deployment windows.
• Usage of modern development tools, version control systems, and architectural diagramming software is constant.

Benefits & growth

• Compensation often includes a significant base salary supplemented by performance-based bonuses or sales commissions.
• Stock options or restricted stock units are common components of the total rewards package in SaaS companies.
• Career progression typically leads to roles such as Principal Architect, Director of Solutions, or Chief Technology Officer.
• Professional development is supported through continuous training on emerging AI technologies and cloud certifications.