Senior AI Enablement Architect

Mai Placement•Newark, NJ

5d•$150,000 - $200,000•Onsite

About The Position

We are seeking a Senior AI Enablement Architect to design and implement the architectural frameworks, standards, and guardrails that govern how AI tools are adopted across the organization. This role sits at the intersection of software engineering, AI, security, and DevOps . You will enable teams to move fast with AI while ensuring systems remain secure, stable, and production-ready. This is a hands-on architecture role for someone who has built real systems—not just evaluated tools.

Requirements

5+ years of software engineering experience
Deep expertise in API design, microservices, and system integration
Strong architectural background designing production-grade systems
Strong fluency with LLMs, prompt engineering, and AI coding agents
Practical understanding of AI risks and mitigation strategies
Security-first mindset with knowledge of OWASP principles applied to AI-generated code
Systems thinker who designs for scale, safety, and speed
Comfortable working cross-functionally with engineers, DevOps, and business stakeholders
Clear communicator who can set standards without slowing teams down
Ownership mindset — builds frameworks that actually get used

Responsibilities

Design safe innovation frameworks that allow experimentation without risking production systems
Define isolation patterns, guardrails, and approval workflows for AI usage
Establish how AI tools interact with core systems, data, and infrastructure
Develop clear standards for AI tool usage, deployment pipelines, and data access
Define policies addressing AI-specific risks such as prompt injection and code-generation vulnerabilities
Ensure architectural consistency across teams and platforms
Set integration standards across services, including API contracts, authentication, authorization, and data governance
Ensure integrations are secure, scalable, and maintainable
Guide teams on architectural tradeoffs and long-term system health
Partner with DevOps to embed AI guardrails into CI/CD pipelines
Implement approval gates, secrets handling, rollout/rollback strategies, and automated scanning
Ensure AI-generated code meets quality and security standards before release
Advise engineering and business teams on AI architecture decisions
Translate constraints into practical, scalable solutions
Build starter kits, templates, shared components, and example integrations teams can reuse