Gen AI Architect

Role: GenAi Architect

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

Year of experience – 10+ experience

JD-

Job Description

Role Summary

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.

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Key Responsibilities

•             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.

Required Qualifications

•             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.

Core Generative AI & ML Expertise

•             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.

AI Architecture & System Design

•             Ability to design scalable, modular GenAI architectures for production use.

•             Experience with:

o            RAG (Retrieval-Augmented Generation) architectures

o            Vector databases (semantic search, embeddings indexing)

o            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.

Product Engineering & Integration

•             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.

Data & Knowledge Engineering

•             Proficiency in data pipelines, feature engineering, and unstructured data processing.

•             Experience with:

o            Knowledge graphs

o            Metadata-driven architectures

o            Document ingestion and chunking strategies

•             Strong understanding of data quality, provenance, and governance for AI systems.

Cloud, MLOps & Platform Skills

•             Strong experience in cloud-native environments (Google Cloud Platform).

•             Familiarity with MLOps practices, including:

o            Model versioning

o            Deployment pipelines

o            Monitoring, logging, and drift detection

•             Experience with containerization, Kubernetes, and CI/CD pipelines.

•             Knowledge of inference optimization and cost-control strategies.

Security, Privacy & Responsible AI

•             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).