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.

How we hire

Same five steps as every Solomon role: intro chat → paid take-home → build review → leadership call → offer.

For questions about this role, email epaez@solomonconsult.com.