layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Sr Mgr, Software Engineering at Walt Disney

Define and own the AI and automation strategy, prioritizing and delivering use cases aligned to business goals while leading end‑to‑end execution—from definition and design through engineering, testing, integration, and production deployment within transformation programs.
Design reusable AI and automation accelerators — patterns, frameworks, and components — that can be deployed across multiple DX businesses, functions, and programs, compounding value at the segment level.
Partner with technology teams to align on AI capability roadmaps, data readiness requirements, and the governance model for AI tools built on the centralized data platform.
Establish delivery standards for AI and automation engineering — including solution design, model governance, testing rigor, deployment reliability, and production monitoring — ensuring quality and sustainability at scale.
Accountabile for engineering quality, technical standards, and scalable solution delivery
Lead, manage, and develop a high-performing team, providing clarity of direction, technical mentorship, and accountability for delivery excellence.
Serve as a trusted strategic partner to senior business leaders across Finance, HR, and other functional areas — helping them understand what AI can and cannot do, building realistic expectations, and co-designing solutions that drive adoption and lasting value.
Communicate AI and automation strategy, progress, and outcomes to executive audiences — including Directors, VPs, and SVPs — through clear, business-grounded narratives that connect technical work to segment-level value.
Required Qualifications & Skills
10+ years of experience in AI, machine learning, data engineering, or automation, with at least 3 years in a leadership role managing technical teams in an enterprise environment.
Proven delivery record taking AI and automation capabilities from concept through production in complex, matrixed organizations — with demonstrated, measurable business outcomes.
Deep technical fluency in applied AI/ML — including large language models, generative AI, agentic workflows, machine learning pipelines, and enterprise automation patterns (RPA, workflow orchestration, intelligent process automation).
Strong business value orientation: the ability to translate technical AI capability into a clear business case, quantify potential value, and track realized outcomes against that case post-deployment.
Experience designing and deploying reusable AI/automation frameworks or accelerators that scale across multiple programs, functions, or business units — not just one-off point solutions.
Demonstrated ability to integrate AI and automation capabilities into enterprise data platforms, operational systems, and business workflows — not just build them in isolation.
Strong cross-functional leadership skills, including the ability to influence senior stakeholders without authority, navigate organizational complexity, and drive alignment across diverse teams.
Experience managing and developing technical teams, including performance management, career development, and building a culture of delivery excellence and continuous learning.
Executive-level communication skills: comfortable translating complex AI concepts into business language for VP and SVP audiences, and building the credibility to be a trusted advisor on AI strategy.
Experience working in a segment-wide or enterprise-wide context, where solutions must scale across multiple business units with differing operational models and technology landscapes.
Technical Expertise
AI & Machine Learning
Applied experience with large language models (LLMs), generative AI, retrieval-augmented generation (RAG), and agentic AI frameworks in enterprise production environments.
Proficiency with ML lifecycle management — including model training, evaluation, deployment, monitoring, and responsible AI governance (bias, fairness, explainability).
Familiarity with AI platforms and tooling such as Azure OpenAI, AWS SageMaker, Databricks, or equivalent enterprise AI infrastructure.
Automation & Integration
Hands-on experience with enterprise automation patterns including RPA, workflow orchestration (e.g., Apache Airflow, Azure Logic Apps), and intelligent process automation.
Experience integrating AI and automation capabilities with enterprise data platforms, ERP/EPM systems, and operational workflows.
Familiarity with data engineering fundamentals — pipelines, transformation, data quality, and the data readiness requirements that underpin AI effectiveness.
Platforms & Tooling
Experience with cloud-native data and AI platforms (Azure, AWS, GCP) and modern data platform architectures (lakehouses, data meshes, semantic layers).
Familiarity with BI and analytics tooling (Tableau, Power BI) and how AI-generated insights are surfaced and consumed by business users.
Proficiency with delivery and collaboration tools including Jira, Confluence, and Smartsheet.

Sr Mgr, Software Engineering

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