Generative AI Architect

Job Title: Generative AI Architect (GenAI Architect)

Job Type: Contract

Location: Cleveland, OH (Hybrid)

Experience: 8+ Years

Job Summary

We are seeking an experienced Generative AI Architect to design, develop, and implement enterprise-grade Generative AI solutions for business applications and product platforms. The ideal candidate will have a strong background in software architecture, AI/ML engineering, cloud technologies, and enterprise application development, with hands-on experience in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI.

The candidate will be responsible for defining end-to-end AI architecture, integrating GenAI capabilities into enterprise systems, and establishing best practices for scalable, secure, and reliable AI application development. This role requires close collaboration with engineering, data, and product teams to translate business requirements into production-ready AI solutions while ensuring performance, security, governance, and responsible AI practices.

Key Responsibilities

• Design and implement end-to-end architecture for Generative AI-powered applications and enterprise product features.
• Develop and integrate AI solutions using LLMs, foundation models, embeddings, and multimodal AI technologies.
• Build and optimize RAG pipelines, vector search solutions, semantic search, and document retrieval systems.
• Design and implement AI agents, multi-agent systems, prompt orchestration, and automated workflows.
• Establish reusable architecture patterns and best practices for prompt engineering, model integration, and inference optimization.
• Integrate GenAI capabilities into customer-facing applications, internal products, APIs, and enterprise platforms.
• Design scalable microservices, event-driven systems, and API-based architectures for AI applications.
• Develop data pipelines for document ingestion, preprocessing, chunking, and unstructured data processing.
• Work with knowledge graphs, metadata-driven architectures, and enterprise data sources to improve AI retrieval and response quality.
• Leverage Google Cloud Platform (GCP) to build, deploy, and manage scalable AI solutions.
• Implement MLOps practices for model versioning, deployment, monitoring, logging, and performance evaluation.
• Optimize model latency, resource utilization, inference performance, and operational costs.
• Implement security controls, AI guardrails, content filtering, and data access restrictions to protect enterprise systems.
• Ensure compliance with data privacy, AI governance, and responsible AI standards.
• Collaborate with software engineers, data engineers, product managers, and business stakeholders to define technical requirements and deliver AI solutions.
• Provide technical leadership and architectural guidance to engineering teams throughout the development lifecycle.

Required Skills

• 8+ years of experience in software architecture, software engineering, ML engineering, or platform engineering.
• At least 2 years of hands-on experience developing or implementing AI/ML solutions, including Generative AI.
• Strong programming experience in Python, Java, or a similar language.
• Strong understanding of Generative AI, LLMs, foundation models, embeddings, and multimodal AI.
• Hands-on experience with GPT-style, Claude-style, LLaMA-style, or similar foundation models.
• Expertise in prompt engineering, prompt orchestration, and AI agent frameworks.
• Strong knowledge of Retrieval-Augmented Generation (RAG), vector databases, semantic search, and embeddings indexing.
• Experience designing scalable AI architectures, multi-agent workflows, and production-ready AI applications.
• Knowledge of microservices, REST APIs, event-driven architectures, and distributed systems.
• Strong experience with Google Cloud Platform (GCP) and cloud-native application development.
• Familiarity with MLOps, CI/CD pipelines, Docker, Kubernetes, and model monitoring.
• Experience with data pipelines, document processing, data quality, and enterprise data governance.
• Understanding of AI security risks, prompt injection, data leakage, guardrails, and responsible AI practices.
• Knowledge of AI evaluation metrics, inference optimization, and model performance monitoring.
• Strong analytical, problem-solving, communication, and technical leadership skills.

Preferred Qualifications

• Experience integrating GenAI capabilities into enterprise products and customer-facing applications.
• Familiarity with knowledge graphs, metadata-driven architectures, and advanced information retrieval techniques.
• Experience with A/B testing, feature flags, and iterative AI product releases.
• Background in analytics engineering, data platforms, or regulated enterprise environments.
• Knowledge of AI governance frameworks, explainability, bias mitigation, and data privacy regulations such as GDPR.