Forward Deployed AI Architect

Crowe•Tampa, FL

4d•$102,400 - $204,100•Hybrid

About The Position

The Forward Deployed AI Architect (Manager) is a hands-on technical authority in Crowe Studio’s Forward Deployed Engineering practice. The role designs, develops, and delivers innovative, production-ready AI solutions for clients, owning solution architecture, technical feasibility, engineering quality, and production readiness while actively coding, prototyping, and solving complex technical problems. The architect works directly with clients and cross-functional teams to turn business challenges into scalable, secure, and maintainable AI applications. Work includes agentic AI systems, generative AI applications, enterprise integrations, cloud-native architectures, and production deployments. The role guides technical decisions, establishes engineering standards, mentors engineers, communicates architectural tradeoffs, and challenges approaches that introduce unacceptable risk or fail production requirements. Crowe Studio provides embedded consulting and engineering to help clients rapidly prove and deploy AI solutions. The team works collaboratively with client teams and other technical disciplines, with an emphasis on measurable outcomes, continuous learning, and sustainable performance.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.
At least 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 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, 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, including translating business requirements into technical solutions, explaining architectural decisions, and mentoring engineering teams.
Demonstrated ownership of technical delivery, engineering quality, and risk management in collaborative, fast-paced environments, with a commitment to hands-on engineering.
Ability to travel up to 80%, depending on client and engagement requirements.
Must verify identity and eligibility to work in the United States; Crowe is not sponsoring work authorization at this time.

Nice To Haves

Master’s degree in a technical field or relevant cloud or architecture certifications.
Experience developing and deploying production AI applications using LLMs or agent frameworks from OpenAI, Anthropic, Google, or similar providers, including tool orchestration and multi-step agentic workflows.
Experience designing evaluation frameworks for LLM and agentic systems, including quality, reliability, safety, performance, and responsible AI considerations.
Advanced cloud and AI infrastructure experience, including Kubernetes, Terraform, observability, and managed AI services such as Azure AI, Amazon SageMaker, or Google Vertex AI.
Experience architecting RAG solutions, integrating vector databases such as Pinecone, Weaviate, or FAISS, and optimizing model inference or AI application performance.
Familiarity with AI-assisted development and low-code automation platforms such as Microsoft Power Platform, Cursor, Replit, Retool, or n8n, and experience supporting client discovery, technical scoping, or proof-of-value engagements.

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 technical feasibility, scalability, security, and alignment with client business objectives.
Remain hands-on across solution development, including 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 LLMs, vector databases, APIs, microservices, enterprise data platforms, and client systems.
Own technical feasibility, solution design decisions, and build quality from initial discovery through production deployment.
Guide solutions through Architecture Review Board processes and ensure alignment with enterprise architecture standards, security requirements, responsible AI principles, and production-readiness expectations.
Assess technical feasibility during client discovery, solution scoping, and sales-support discussions; communicate technical tradeoffs, risks, and recommended approaches.
Establish engineering standards, reusable architectural patterns, technical documentation, and development best practices across client engagements.
Design solutions with 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 tradeoffs among custom development, managed AI services, and low-code or automation platforms to select appropriate implementation strategies.
Identify and address risks involving performance, scalability, security, reliability, and cost.
Mentor engineers through architecture reviews, code reviews, technical guidance, and hands-on problem-solving.
Evaluate emerging AI frameworks, cloud capabilities, and engineering tools for innovation and continuous improvement.