This role is focused on post-sales adoption, architectural integrity, and repeatability. You will create the reference architectures, accelerators, guardrails, and implementation blueprints that help Forward Deployed Engineers and delivery teams implement AI solutions consistently while managing risk, cost, security, and operational readiness.

Essential Functions of the Job

Define Fabric AI reference architectures covering AI Factory, OpenAI integrations, GenAI workloads, model orchestration, agentic patterns, retrieval-based approaches, deployment, evaluation, monitoring, and lifecycle management

Create repeatable AI design patterns for common post-sales scenarios such as GenAI application onboarding, secure model access, prompt orchestration, RAG-style solution patterns, evaluation workflows, telemetry, and operational controls

Develop AI accelerators including architecture blueprints, starter kits, integration templates, deployment patterns, evaluation frameworks, monitoring patterns, reusable prompts, configuration guidance, and handoff packs

Ensure AI accelerators are easy to consume by Forward Deployed Engineers, delivery teams, and segment-aligned teams

Define guardrails for responsible AI delivery, including access controls, data boundaries, auditability, monitoring, human oversight, and operational risk management

Partner with AI Product, Engineering, Security, Risk, and Reliability teams to align post-sales patterns with platform roadmap and governance expectations

Act as the senior escalation point for complex AI architecture questions that go beyond standard implementation playbooks

Convert recurring AI implementation challenges into reusable patterns, playbooks, and accelerators

Define clear handoff expectations between Presales, Domain Architecture, Forward Deployed Engineering, and ongoing support teams





Govern exceptions to standard AI patterns and determine whether new supported patterns are required

Maintain AI architecture assets in agreed EY Fabric knowledge repositories such as GitHub, SharePoint, Teams, and internal knowledge hubs

Measure AI architecture effectiveness through pattern adoption, reduction in bespoke builds, implementation speed, operational quality, and reuse of accelerators

Mentor mid-level AI Architects and help build post-sales AI architecture maturity across EY Fabric

Knowledge and Skills Required

Strong experience designing and governing production AI, ML, or GenAI systems

Familiarity with AI Factory concepts, OpenAI-style integrations, model orchestration, RAG patterns, evaluation, monitoring, and lifecycle management

Strong cloud architecture fundamentals including identity, networking, security, logging, and observability

Understanding of responsible AI, data governance, security, risk, and operational controls

Experience creating reusable AI architecture assets, templates, implementation guides, or accelerators

Ability to communicate AI architecture decisions, tradeoffs, and risks to technical and executive stakeholders

Strong ability to work across Product, Engineering, Security, Risk, Growth, and delivery teams

Job Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or equivalent experience

10+ years of engineering with recent GenAI architecture experience, including significant AI, ML, GenAI, or platform delivery experience

Fully bilingual (english and spanish)

Experience building reusable AI patterns, accelerators, technical standards, or implementation playbooks

AI, cloud architecture, security, or data certifications preferred

Offer

Hybrid model

Superior adaptable benefits

100% payroll schema

📌 Senior Domain Architect (Ciudad de México)
🏢 Tech Mahindra
📍 Ciudad de México