GenAI Architect - Enterprise AI Platform Leader

Swanktek Inc; City of Albany (NY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Swanktek Inc. is seeking a GenAI Architect to design and implement enterprise-grade GenAI solutions embedded in product platforms.

You will bridge AI research, engineering, and product development to deliver scalable, secure, and business-aligned AI capabilities. Responsibilities include owning end-to-end GenAI architecture, guiding engineering teams, and establishing standards for prompts, agents, and inference layers while ensuring privacy, compliance, and cost-aware deployment in a hybrid,

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.

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.
• Translate product requirements into AI-driven capabilities and technical designs.
• Familiarity with data pipelines, knowledge graphs, and document ingestion strategies.

Skills

GenAI architecture

AI governance

System design

ML fundamentals

Data pipelines

Tools

Python

Java

Kubernetes

CI/CD

Vector databases

Job description

Swanktek Inc. is seeking a GenAI Architect to design and implement enterprise-grade GenAI solutions embedded in product platforms.

You will bridge AI research, engineering, and product development to deliver scalable, secure, and business-aligned AI capabilities. Responsibilities include owning end-to-end GenAI architecture, guiding engineering teams, and establishing standards for prompts, agents, and inference layers while ensuring privacy, compliance, and cost-aware deployment in a hybrid,