Accellor - Enterprise Architect - Data & AI

Job Description :

Key Responsibilities :

Strategic Technology Foresight & AI Roadmapping :

- Develop and operationalize an integrated AI & Emerging Tech strategy covering Machine Learning, Generative AI, LLMs, Cloud-native platforms, Edge Computing, APIs, and automation frameworks.

- Conduct strategic assessments on evolving technology trends (cloud modernization, data mesh, low code/no-code platforms) and their impact on capability design, operating models, and workforce transformation.

Use Case Architecture & Value Prioritization :

- Translate ambiguous business challenges into AI-enabled use cases, performing technical feasibility studies

and defining value hypotheses.

- Build and maintain a prioritized AI use case backlog tied to enterprise strategy, supported by ROI-driven phasing and maturity mapping.

Portfolio & Capability Transformation Planning :

- Define the future-state capability portfolio, including shifts in tooling, data integration, automation layers, and human-AI interaction models.

- Lead the design of AI skilling pathways to prepare workforce roles (analysts, testers, designers, change agents) for the future of human-AI collaboration.

Governance, Architecture & Platform Integration :

- Establish robust AI governance frameworks, covering explainability, data stewardship, lifecycle ownership, and compliance.

- Partner with Enterprise Architecture and Digital Platforms teams to ensure AI initiatives align with enterprise standards, DevSecOps practices, and target-state platform convergence.

- Advocate for integration of LLM-based copilots, automation scripts, and AI accelerators into enterprise ecosystems (e.g., Azure DevOps, ServiceNow, SAP).

Market & Ecosystem Leadership :

- Monitor market shifts and vendor roadmaps (Microsoft, AWS, ServiceNow, Google) to inform build vs. buy decisions.

- Shape executive narratives, investment cases, and transformation blueprints to secure alignment and sponsorship.

Required Qualifications & Skills :

- 12+ years of experience in AI, emerging technology, or enterprise architecture roles, with leadership exposure.

- Deep expertise in AI/ML concepts, model lifecycle management, LLMs, MLOps, and enterprise integration patterns.

- Strong knowledge of cloud computing platforms (Azure, AWS, GCP), edge architectures, API management, and automation frameworks (RPA, low-code).

- Proven experience in tech portfolio rationalization, roadmap design, and capability operating model development.

- Familiarity with TOGAF, Zachman, SAFe, ITIL frameworks.

- Ability to thrive in ambiguity and shape strategies in large-scale, federated organizations.

- Excellent communication, stakeholder management, and storytelling skills.

Preferred :

- Exposure to business analysis, quality engineering, or experience design practices.

- Experience leading enterprise AI adoption initiatives in regulated/global organizations.