Forward Deployed AI Architect

Crowe•Chicago, IL

3d•Hybrid

About The Position

Crowe Studio’s Forward Deployed Engineering practice embeds consulting and engineering with client teams to help them identify AI opportunities, develop solutions in their own environments, and achieve measurable results. Engagements include short AI Immersion Labs and multi-week Proof of Value Sprints, with an emphasis on rapid execution, technical integration, and demonstrated business value. The Forward Deployed AI Architect (Manager) is a hands-on technical authority responsible for designing, developing, and delivering innovative, production-ready AI solutions for clients. The role owns solution architecture, technical feasibility, engineering quality, and production readiness while participating directly in coding, prototyping, and complex technical problem-solving. It translates business challenges into scalable, secure, and maintainable AI applications across agentic AI, generative AI, enterprise integrations, cloud-native architectures, and production deployments. The architect collaborates with clients and cross-functional teams, guides technical decisions, establishes engineering standards, mentors engineers, and communicates architectural tradeoffs. The role spans discovery through production deployment and includes assessing risks, ensuring responsible AI and security considerations, and challenging approaches that could create unacceptable risk or fail to meet production requirements. Travel may be required up to 80%, depending on client and engagement needs. The role is hybrid.

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 modern programming languages, application development, and designing and deploying 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 engineering decisions that account for 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 complex 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.
Ability to 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.
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 solutions are feasible, scalable, secure, and aligned with client business objectives.
Remain hands-on in application coding, data integration, prototyping, debugging, and technical implementation.
Design and implement cloud-native AI solutions using Azure, AWS, 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 tradeoffs, risks, and recommended approaches.
Establish engineering standards, reusable architectural patterns, technical documentation, and development best practices across engagements.
Design evaluation frameworks, monitoring, observability, guardrails, and performance optimization for 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 technical 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.