AI Solutions Architect

Franklin Fitch; Texas; Presencial; USD 150.000 - 210.000; Tempo integral

Resumo da vaga

Franklin Fitch is seeking an AI Solutions Architect to design and shape enterprise AI infrastructure and solutions. The role focuses on developing solution architectures across applications, models, data platforms, and deployment environments within enterprise settings.

The ideal candidate has broad technical knowledge with deeper expertise in several domains and meaningful exposure to modern AI technologies, working across engineering teams and stakeholders.

Qualificações

• 5+ years of experience in software engineering, solutions architecture, DevOps, ML engineering, platform engineering, or a related technical discipline.

Responsabilidades

• Design and architect enterprise AI solutions spanning applications, models, data platforms, infrastructure, and deployment environments.
• Translate technical and business requirements into scalable architecture designs, diagrams, and technical documentation.
• Evaluate technologies and architectural tradeoffs involving performance, scalability, reliability, availability, and security.
• Guide engineering teams on technical direction and support implementation of proposed architectures.
• Assess emerging AI technologies, frameworks, and platforms to determine how they can be applied within enterprise environments.
• Engage with customers and internal teams to understand technical environments, discuss proposed solutions, and occasionally demonstrate workflows or capabilities.
• Collaborate across AI, software engineering, infrastructure, and platform teams to deliver integrated solutions.

Descrição da vaga

Enterprise AI | Infrastructure | Architecture & Engineering

About the Role

We are partnering with a growing technology organization expanding its enterprise AI infrastructure and solutions capabilities. They are looking for an AI Solutions Architect to design and shape technical solutions across AI applications, models, infrastructure, data platforms, and enterprise environments.

This is a technically focused, delivery-oriented role suited to someone with a strong engineering background and experience designing complex systems. You will work across the broader AI technology stack, developing solution architectures, evaluating technical approaches, and guiding engineering teams through implementation.

The ideal candidate has broad technical knowledge across several domains, with deeper expertise in two or three areas, alongside meaningful recent exposure to modern AI technologies.

Responsibilities

• Design and architect enterprise AI solutions spanning applications, models, data platforms, infrastructure, and deployment environments.
• Translate technical and business requirements into scalable architecture designs, diagrams, and technical documentation.
• Evaluate technologies and architectural tradeoffs involving performance, scalability, reliability, availability, and security.
• Guide engineering teams on technical direction and support implementation of proposed architectures.
• Assess emerging AI technologies, frameworks, and platforms to determine how they can be applied within enterprise environments.
• Engage with customers and internal teams to understand technical environments, discuss proposed solutions, and occasionally demonstrate workflows or capabilities.
• Collaborate across AI, software engineering, infrastructure, and platform teams to deliver integrated solutions.

Required Experience

• 5+ years of experience in software engineering, solutions architecture, DevOps, ML engineering, platform engineering, or a related technical discipline.
• Experience designing or architecting complex technical solutions across multiple technology domains.
• Meaningful recent exposure to modern AI technologies, ideally through professional projects, personal experimentation, or hands-on development.
• Understanding of how AI applications, models, infrastructure, and data platforms integrate within enterprise environments.
• Ability to develop architecture diagrams, communicate technical decisions, and explain the tradeoffs behind design choices.
• Comfortable working across engineering teams and discussing technical concepts with both technical and non-technical stakeholders.

Technical Background

Experience across several of the following areas is valuable. Candidates are not expected to be experts in every technology.

• AI Infrastructure & Platforms: GPU infrastructure, Kubernetes, Docker, container orchestration, distributed systems, MLOps, model serving, cloud-native platforms.
• Software Engineering & DevOps: Python, APIs, microservices, system design, Git, CI/CD, Terraform, Ansible, automation, observability.
• Data Platforms: Data lakes, data warehouses, data engineering, data pipelines, SQL/NoSQL databases, Databricks, Snowflake, vector databases.
• Enterprise Infrastructure: Cloud and hybrid environments, compute, storage, virtualization, networking fundamentals, security, and deployment architecture.

Ideal Candidate

Someone with a strong technical foundation who can connect different parts of the technology stack and understand how they work together. You may come from software engineering, DevOps, ML engineering, AI infrastructure, or solutions architecture, but you should have a genuine interest in modern AI and be able to demonstrate depth in a few technical areas.