Enterprise AI Architect - Forward-Deployed, Hands-On

Crowe; Chicago (IL); Hybrid; USD 102,000 - 204,000; Full time

Benefits offered by this job

Unlimited PTO

Flexible remote work policy

Competitive compensation

Job summary

Crowe Studio seeks a Forward Deployed AI Architect (Manager) to lead the design, development, and deployment of production AI solutions for clients. You’ll own architecture, feasibility, security, and production readiness while remaining hands‑on with coding and prototyping.

You will guide teams across AI engineering, data engineering, MLOps, and DevOps, partnering with clients to deliver scalable, secure enterprise AI workflows and integration patterns. Travel up to 80% may be required.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field.
• Minimum of 6 years of experience in production software engineering, cloud engineering, AI/ML engineering, or a related technical discipline.
• Strong hands‑on software engineering experience, including proficiency in modern programming languages, application development, and the design and deployment of production‑grade solutions.
• Demonstrated experience architecting cloud‑native applications and distributed systems using Azure, AWS, or GCP, including APIs, microservices, enterprise integrations, and data workflows.
• Working knowledge of generative AI and LLM application patterns, including prompt engineering, retrieval‑augmented generation (RAG), embeddings, vector search, and agentic workflows.
• Ability to assess technical feasibility, evaluate architectural tradeoffs, and make sound engineering decisions considering scalability, security, performance, reliability, and cost.
• Understanding of software and AI application lifecycle management, including testing, evaluation, production deployment, monitoring, and operational support.
• Strong communication and client‑facing skills, with demonstrated ability to translate business requirements into technical solutions, explain complex architectural decisions, and mentor engineering teams.

Responsibilities

• Architect and develop end‑to‑end applied AI solutions, including generative AI applications, agentic systems, retrieval‑augmented generation (RAG), and AI‑enabled enterprise workflows.
• Lead solution architecture for AI build and proof‑of‑concept engagements, ensuring solutions are technically feasible, scalable, secure, and aligned with client business objectives.
• Remain hands‑on across solution development, including application coding, data integration, prototyping, debugging, and technical implementation.
• Design and implement cloud‑native AI solutions using Microsoft Azure, Amazon Web Services (AWS), Google Cloud Platform (GCP), and relevant AI platforms and services.
• Define architecture and integration patterns for large language models (LLMs), vector databases, APIs, microservices, enterprise data platforms, and client systems.
• Take ownership of technical feasibility, solution design decisions, and build quality from initial discovery through production deployment.
• Guide solutions through Architecture Review Board processes, ensuring alignment with enterprise architecture standards, security requirements, responsible AI principles, and production readiness expectations.
• Assess technical feasibility during client discovery sessions, solution scoping, and sales support discussions, clearly communicating technical tradeoffs, risks, and recommended approaches.
• Establish engineering standards, reusable architectural patterns, technical documentation, and development best practices across client engagements.
• Design solutions with appropriate evaluation frameworks, monitoring, observability, guardrails, and performance optimization to support reliable AI operations.
• Collaborate with AI engineering, data engineering, MLOps, DevOps, cloud infrastructure, and security teams to deliver integrated enterprise solutions.
• Evaluate technical tradeoffs among custom development, managed AI services, and low‑code or automation platforms to select appropriate implementation strategies.
• Identify and address technical risks, including performance, scalability, security, reliability, and cost considerations.
• Mentor engineers through architecture reviews, code reviews, technical guidance, and hands‑on problem‑solving to strengthen engineering quality and team capabilities.
• Evaluate emerging AI frameworks, cloud capabilities, and engineering tools to identify opportunities for innovation and continuous improvement.

Skills

Cloud engineering

AI/ML engineering

Software architecture

Client-facing skills

Team mentoring

Hands-on coding

Education

Bachelor's degree in Computer Science or related field

Tools

Azure

AWS

GCP

LLMs

Vector databases

APIs & microservices

Kubernetes

Terraform

Observability tools

Power Platform / low-code

Job description

Crowe Studio seeks a Forward Deployed AI Architect (Manager) to lead the design, development, and deployment of production AI solutions for clients. You’ll own architecture, feasibility, security, and production readiness while remaining hands‑on with coding and prototyping.

You will guide teams across AI engineering, data engineering, MLOps, and DevOps, partnering with clients to deliver scalable, secure enterprise AI workflows and integration patterns. Travel up to 80% may be required.