Gen AI Architect

Role:
Gen Ai Architect

Location :

-
Columbus, OH;
Cleveland, OH;
Kansas City, KS; or Albany, NY . (Hybrid)

Year of experience — 10 experience

The GenAI Architect is responsible for designing, guiding, and implementing 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.

• 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.

• 8 years of experience in software architecture, ML engineering, or platform engineering.
• 2 years of hands-on experience 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.

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

• Ability to design scalable, modular GenAI architectures for production use.
• Experience with:
  • RAG (Retrieval-Augmented Generation) architectures
  • Vector databases (semantic search, embeddings indexing)
  • Multi-agent systems and workflow orchestration
• Strong understanding of low-latency inference, model routing, and fallback strategies.
• Knowledge of event-driven, microservices, and API-first architectures.

• Experience integrating GenAI capabilities into customer-facing and internal products.
• Ability to translate product requirements into AI-driven capabilities and technical designs.
• Familiarity with A/B testing, feature flags, and iterative product releases involving AI.

• Proficiency in data pipelines, feature engineering, and unstructured data processing.
• Experience with:
  • Knowledge graphs
  • Metadata-driven architectures
  • Document ingestion and chunking strategies
• Strong understanding of data quality, provenance, and governance for AI systems.

• Strong experience in cloud-native environments (Google Cloud Platform).
• Familiarity with MLOps practices, including:
  • Model versioning
  • Deployment pipelines
  • Monitoring, logging, and drift detection
• Experience with containerization, Kubernetes, and CI/CD pipelines.
• Knowledge of inference optimization and cost-control strategies.

• Understanding of AI security risks (prompt injection, data leakage, model abuse).
• Experience implementing guardrails, content filters, and policy enforcement.
• Knowledge of responsible AI practices, including explainability, bias mitigation, and compliance.
• Familiarity with data privacy regulations (e.g., GDPR, enterprise governance standards).