Enterprise AI Architect

Job DescriptionJob Description

Enterprise AI Architect

*This is a Hybrid role - 3 days in office (Charlotte or Hartford - NO REMOTE OPTION)

$85-$89/hr. W2

Contract to hire

Role Overview:

Seeking a highly skilled, hands-on AI Architect to support the Enterprise Technology Architecture (ETA) organization. This role will be responsible for leading the design, governance, and implementation of AI-centric technology architectures across a hybrid infrastructure landscape, including AWS, Google Cloud Platform (GCP), and on premises data centers.

The AI Architect will play a critical role in enabling the responsible and secure adoption of Generative AI (GenAI) technologies, establishing architectural standards, and driving the implementation of multiple internal-facing GenAI use cases. This role requires a strong blend of strategic architectural background and hands-on technical execution.

Key Responsibilities:

Architecture & Strategy

• Design and develop Agentic AI solutions leveraging Google ADK, LangGraph/Langchain and Agent Engine on Google Cloud Platform (GCP).

• Deliver innovative AI capabilities that enhance business processes and customer experiences through GenAI and Agentic AI frameworks.

• Ensure AI solutions align with enterprise technology strategy and meet scalability, security, and compliance requirements.

• Drive adoption of GenAI and Agentic AI frameworks across business units.

• Conduct proof-of-concepts (POCs) for emerging AI technologies and frameworks.

• Collaborate with enterprise architects to ensure AI solutions align with technology strategy and reference architectures.

• Stay current with AI trends, frameworks, and best practices to propose innovative solutions.

Cloud Security (AWS & GCP)

• Architect and implement secure cloud solutions leveraging services and third-party tools.

• Define and enforce cloud security posture management (CSPM), and access management (IAM), and encryption strategies.

• Collaborate with DevOps and cloud engineering teams to embed security into CI/CD pipelines and infrastructure-as-code.

Datacenter & Hybrid Security

• Ensure secure integration between cloud platforms and on-prem datacenters, including network segmentation, VPNs, and secure data flows.

• Oversee security controls for legacy systems and their modernization paths.

GenAI Security Enablement

• Define security and governance frameworks for GenAI platforms and use cases.

• Ensure responsible AI practices including data privacy, model integrity, and ethical AI usage.

• Collaborate with AI/ML teams to secure model training, inference, and deployment pipelines.

Governance & Collaboration

• Serve as a key member of the Enterprise Technology & Solution Governance

• Partner with business, IT, and risk stakeholders to align security architecture with enterprise goals.

• Provide technical guidance and mentorship to junior engineers and architects on AI development practices.

Required Qualifications:

Qualifications

• Experience: 10-12 years in Software Engineering, with at least 2+ years in GenAI and Agentic AI development.

• Project Delivery: Must have delivered at least one GenAI or Agentic AI project end-to-end.

• Technical Expertise:

• Strong proficiency in Google ADK, LangGraph/Langchain, Agent Engine, and Vertex AI.

• Hands-on experience with GCP services: Cloud Run, ECS, Vertex AI Search Engine, IAM, and networking.

• Solid understanding of GenAI patterns, LLM fine-tuning, and prompt engineering.

• Programming Skills: Python, Java, or similar for AI development.

• Cloud Certifications: GCP Professional Machine Learning Engineer or GCP Professional Cloud Architect .

• Education: Bachelor’s or Master’s degree in Computer Science, AI/ML, or related field.

• Soft Skills: Strong problem-solving, communication, and collaboration skills.

Key Competencies:

• Strategic and analytical thinking

• Successfully integrated AI agents into business or technical workflows for automation and enhanced decision-making.

• Improved operational efficiency and customer experience through AI-driven innovation.

• Established reusable AI patterns and best practices for enterprise adoption.

• Strong communication and stakeholder engagement

• Proactive and solution-oriented mindset