Role summary The GenAI Architect designs, guides, and implements enterprise-grade Generative AI solutions embedded within product platforms. This role bridges AI research, engineering, and product development — ensuring GenAI capabilities are scalable, secure, and aligned with business objectives.

Location & eligibility

- On-site in Cleveland, Ohio (not remote).
- Candidates must be a U.S. citizen or hold a valid U.S. work visa / work authorization .

What you'll do

- Design and own the end-to-end architecture for GenAI-powered product features.
- Guide engineering teams on best practices for GenAI development and integration.
- Establish standards and patterns for prompts, agents, and inference layers.
- Ensure security, compliance, and responsible AI principles are built into every solution.

Core skillset Generative AI & ML

- Strong understanding of Generative AI models (LLMs, multimodal models, embeddings).
- Hands-on experience with foundation models (GPT-style, Claude-style, LLaMA-style) and model adaptation techniques.
- Expertise in prompt engineering, prompt orchestration, and agent-based frameworks.
- Solid grounding in ML fundamentals: supervised/unsupervised learning, evaluation metrics, inference optimization.

AI architecture & system design
- Design scalable, modular GenAI architectures for production.
- Experience with RAG architectures, vector databases, multi-agent systems, and workflow orchestration.
- Low-latency inference, model routing, and fallback strategies.
- Event-driven, microservices, and API-first architectures.

Product engineering & integration




- Integrate GenAI into customer-facing and internal products.
- Translate product requirements into AI-driven capabilities and technical designs.
- Familiarity with A/B testing, feature flags, and iterative AI product releases.

Data & knowledge engineering
- Data pipelines, feature engineering, unstructured data processing.
- Knowledge graphs, metadata-driven architectures, document ingestion and chunking strategies.
- Data quality, provenance, and governance for AI systems.

Cloud, MLOps & platform
- Strong experience in cloud-native environments (GCP).
- MLOps: model versioning, deployment pipelines, monitoring, logging, drift detection.
- Containerization, Kubernetes, CI/CD.
- Inference optimization and cost-control strategies.

Security, privacy & responsible AI
- AI security risks (prompt injection, data leakage, model abuse).
- Guardrails, content filters, and policy enforcement.
- Responsible AI: explainability, bias mitigation, compliance.
- Familiarity with data privacy regulations (e.g. GDPR) and enterprise governance.

Required qualifications

- 8+ years in software architecture, ML engineering, or platform engineering.
- 2+ years hands-on with AI/ML systems, including Generative AI.
- Strong software engineering background (Python, Java, or similar).
- Prior work with enterprise AI governance or regulated industries.
- Familiarity with open-source AI ecosystems.
- Background in data platforms or analytics engineering.
- U.S. citizenship or valid U.S. work authorization (visa) — on-site Cleveland, OH.

📌 Gen AI Architech (Monterrey)
🏢 TeamUp
📍 Monterrey