Location: Remote

Experience: 7–10+ years overall IT experience, including 1–2+ years driving AI adoption practices

Employment Type: Contract Full time

About the Role

We are looking for an experienced System/Domain/Enterprise Architect who has evolved into an AI Adoption and Enablement leader within engineering organizations. Our client is currently relying on a tool/process that involves significant manual intervention and is prone to recurring bugs and breakages. We need someone who can guide the client on how to modernize this using AI — evaluating where AI tools and models can reduce manual effort, improve reliability, and be architected into their enterprise systems in a scalable, sustainable way.

This role is for someone who understands enterprise/systems architecture deeply and has spent the last 1–2 years actively building AI-driven practices, tools, and workflows to improve engineering and operational productivity — not just experimenting with AI, but embedding it into real environments with measurable business impact.

Important: This role requires a System Engineer / IT domain background (infrastructure, enterprise architecture, systems integration, DevOps). This is not a Data Science or ML Engineer role — we are not looking for candidates whose primary background is data science, model building/training, or statistical modeling.

You'll be responsible for defining how AI tools and practices get adopted across the client's engineering and operations teams, evaluating and recommending the right tools, and creating a repeatable framework — backed by data on productivity and reliability gains — for how AI can be built into their enterprise systems going forward.

Key Responsibilities

- Assess the client's current tool/process (currently manual-intervention-heavy,



with frequent bugs and breakages) and design an AI-driven approach to reduce manual effort and improve system reliability.
- Design and drive an enterprise-wide AI adoption strategy for the client's IT/engineering teams, aligned with business and technology goals.
- Evaluate, pilot, and recommend AI-powered tools (code assistants, AI-augmented testing, code review, documentation, DevOps/IT automation, etc.) based on real use-case fit — not hype.
- Define measurable outcomes (reduction in manual effort, defect/breakage rates, cycle time, deployment frequency, reliability, team satisfaction) to track the impact of AI adoption.
- Partner with the client's engineering, architecture, and platform teams to integrate AI tools into existing systems, workflows, and CI/CD pipelines.
- Establish governance, best practices, and guardrails for responsible AI usage (security, IP, data privacy, model reliability).
- Act as a bridge between enterprise/systems architecture and AI capabilities — ensuring AI recommendations align with the client's existing system, domain, and integration architecture.
- Create internal playbooks, training material, and enablement sessions to drive AI tool adoption across teams.
- Stay current with the evolving AI tooling landscape (LLM-based coding assistants, agentic frameworks, MLOps/LLMOps practices) and translate relevant advances into practical org-level adoption.
- Present adoption outcomes, ROI,



and roadmap recommendations to senior leadership/stakeholders.

Required Skills & Experience

- 7–10+ years of IT experience as a System Engineer, System Architect, Domain Architect, or Enterprise Architect — strictly from a systems/infrastructure/IT background ( not data science or ML ).
- 1–2+ years of hands-on experience building or leading AI adoption practices within an IT/engineering organization (not just personal use of AI tools, but establishing practices for teams).
- Practical experience evaluating and rolling out AI tools (e.g., GitHub Copilot, Claude Code, Cursor, Amazon Q Developer, or similar) at scale.
- Strong understanding of enterprise systems, IT operations/DevOps practices, and how AI tools/models can be integrated into existing manual or legacy workflows to reduce errors and breakages.
- Ability to define and track measurable outcomes — you think in terms of KPIs and business impact, not just tool adoption for its own sake.
- Solid grasp of enterprise architecture principles (TOGAF or equivalent experience is a plus) and how AI initiatives fit within broader IT strategy.
- Excellent communication skills — able to guide client stakeholders and present to leadership with equal ease.
- Experience with change management or driving adoption of new technology/practices across teams is a strong plus.

Nice to Have

- Familiarity with LLMOps, RAG architectures, or agentic AI frameworks.
- Experience with governance/compliance considerations around AI usage in enterprise settings.
- Prior experience in a Center of Excellence (CoE) or platform engineering role.

Want me to also trim it down to a shorter LinkedIn-friendly version, since the full one is fairly long for a LinkedIn post?

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