Accellor - Enterprise Architect - Data & AI
7 days ago
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.
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