Forward Deployed AI Architect

Crowe Advisory LLC•Dallas, TX

3d•$102,400 - $204,100•Hybrid

About The Position

As a Forward Deployed AI Architect (Manager) in Crowe Studio’s Forward Deployed Engineering practice, you will serve as a hands-on technical authority responsible for designing, developing, and delivering production-ready AI solutions for clients. You will own solution architecture, technical feasibility, engineering quality, and production readiness while actively coding, prototyping, and solving complex technical problems. You will work directly with clients and cross-functional teams to turn business challenges into scalable, secure, and maintainable AI applications. The work includes agentic AI systems, generative AI applications, enterprise integrations, cloud-native architectures, and production deployments. You will guide technical decisions, establish engineering standards, mentor engineers, and explain architectural tradeoffs, while challenging approaches that introduce unacceptable risk or fail to meet production requirements. Crowe Studio is a business unit focused on helping clients transform and scale through rapid execution, technology integration, embedded AI, and global delivery resources. The Forward Deployed Engineering practice embeds engineers with client teams to identify opportunities, prototype and deploy solutions, and pursue measurable outcomes. Engagements can range from short AI Immersion Labs to multi-week Proof of Value Sprints. The team emphasizes technical and strategic growth, professional development, knowledge-sharing, and a distributed culture grounded in trust and flexibility. The role is hybrid, with travel of up to 80% depending on client and engagement needs. The estimated annual wage range is $102,400 to $204,100.

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 design and deployment of production-grade solutions.
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 involving 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.

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 LLM and agentic-system evaluation frameworks that address quality, reliability, safety, performance, and responsible AI.
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, RAG, and AI-enabled enterprise workflows.
Lead architecture for AI build and proof-of-concept engagements, ensuring technical feasibility, scalability, security, and alignment with client objectives.
Remain hands-on with 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 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 feasibility during client discovery, solution scoping, and sales support, and 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.
Evaluate custom development, managed AI services, and low-code or automation platforms to select suitable implementation strategies.
Identify and address performance, scalability, security, reliability, and cost risks.
Mentor engineers through architecture and code reviews, technical guidance, and hands-on problem-solving.
Evaluate emerging AI frameworks, cloud capabilities, and engineering tools for innovation and continuous improvement.
Travel up to 80%, depending on client and engagement requirements.