Gen AI Architect

VDart Inc; Cleveland (OH); Hybrid; USD 96,000 - 165,000; Full time

Job summary

VDart Inc. is seeking a Gen AI Architect to design and implement enterprise-grade Generative AI solutions embedded within product platforms. You will bridge AI research, engineering, and product development to ensure scalable, secure GenAI capabilities aligned with business goals.

The role requires leading architecture design, guiding engineering teams, and establishing standards for prompts, agents, and inference layers, with a focus on responsible AI, governance, and cost-efficient deployment.

Qualifications

• :8+ years of experience in software architecture, ML engineering, or platform engineering.
• :2+ years 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.

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.

Skills

Software architecture

AI/ML experience

Python

Java

Open-source AI

Data platforms

GenAI models

Tools

GPT-style models

LLaMA-style

Embedding techniques

Job description

Job Title: Gen AI Architect
Location: Cleveland, OH / Mason, OH / Kansas City, KS / Albany, NY (Hybrid)
Job Type: Contract

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.

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:

• 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.

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:
• Knowledge graphs
• Metadata-driven architectures
• 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 (GCP).
• 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.

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).