Engineer 6, Machine Learning, Data & AI

Agentic AI Transformation of Data Engineering:

Lead the strategy for integrating Agentic AI across the enterprise data engineering lifecycle, enabling AI-assisted development, operations, and platform management.

Responsibilities include:

• Define the enterprise roadmap for incorporating AI agents into data engineering workflows, platform operations, and software delivery.
• Partner with platform engineering teams to integrate AI capabilities into CI/CD, Infrastructure-as-Code (IaC), and DevSecOps practices.
• Evaluate emerging agent frameworks, copilots, and autonomous engineering platforms for enterprise adoption.
• Establish best practices and governance for AI-assisted engineering and operational processes.

Enterprise Data Strategy for AI & Agentic Systems:

Develop and drive the enterprise strategy for preparing data assets to support AI, Generative AI, and Agentic AI use cases.

Responsibilities include:

• Define principles and reference architectures that enable AI agents to discover, access, understand, and act upon enterprise data safely and effectively.
• Partner with business and technology leaders to identify high-value opportunities for Agentic AI solutions.
• Establish enterprise standards that align data, AI, and business strategies.

Data Architecture & AI Readiness

Lead architectural efforts to ensure enterprise data is optimized for both human and AI consumption.

Responsibilities include:

• Establish standards that ensure data is:
  • Discoverable
  • Well-described and semantically rich
  • Governed and trusted
  • Accessible through standardized interfaces
  • Consumable by both people and AI agents
• Drive adoption of metadata-driven architectures, semantic models, knowledge graphs, and business ontologies.
• Ensure enterprise data products support machine-to-machine interactions in addition to traditional analytics use cases.

Agentic Data Enablement:

Define the frameworks that enable AI agents to effectively interact with enterprise data and knowledge assets.

Responsibilities include:

• Establish standards for exposing enterprise data through APIs, semantic layers, data products, and retrieval systems.
• Partner with platform teams to develop capabilities supporting:
  • Retrieval-Augmented Generation (RAG)
  • Agent orchestration platforms
  • Tool and API discovery
  • Vector-based retrieval architectures
  • Context management and memory frameworks
• Develop patterns that allow AI agents to access enterprise knowledge securely and responsibly.

Data Governance & Trust:

Ensure governance and trust frameworks evolve to support autonomous and AI-assisted decision making.

Responsibilities include:

• Establish controls for data lineage, provenance, quality, explainability, and auditability.
• Partner with Security, Privacy, and Risk teams to implement responsible AI controls and secure data access practices.
• Define trust frameworks that enable AI agents to operate within approved business guardrails.

Semantic Layer & Knowledge Management:

Drive the development of enterprise semantic capabilities that improve data accessibility and AI reasoning.

Responsibilities include:

• Lead development of enterprise semantic models and shared business definitions.
• Improve metadata quality, business context, and knowledge accessibility across the organization.
• Advance enterprise knowledge management practices that enhance AI reasoning, discovery, and decision support.

Platform & Ecosystem Alignment:

Collaborate across teams to ensure enterprise platforms support AI-native consumption patterns.

Responsibilities include:

• Partner with Data Platform, Engineering, Analytics, and Product teams to align technology roadmaps.
• Collaborate with BI and analytics teams to maintain consistent business metrics and semantic definitions across human and AI consumers.
• Influence technology investments that enable future AI and Agentic AI capabilities.

Innovation & Thought Leadership

Serve as a strategic thought leader on AI readiness, data modernization, and enterprise architecture.

Responsibilities include:

• Monitor emerging trends in AI, Agentic Systems, Data Architecture, and Knowledge Management.
• Evaluate innovative technologies and identify strategic adoption opportunities.
• Advise executive leadership on enterprise AI strategy and future-state architecture.

Qualifications

• + years of experience in Data Architecture, Data Engineering, Enterprise Architecture, or related technology disciplines.
• Proven experience designing and scaling enterprise data ecosystems.
• Experience supporting AI, machine learning, advanced analytics, or data modernization initiatives.
• Demonstrated success influencing outcomes across large, matrixed organizations.

Technical Expertise

• Deep understanding of modern data architectures, including data warehouses, data lakes, lakehouses, and data mesh concepts.
• Expertise in metadata management, semantic modeling, data governance, and data product design.
• Strong understanding of APIs, event-driven architectures, and interoperability standards.
• Experience with AI technologies, including LLMs, RAG architectures, vector databases, agent frameworks, and AI orchestration platforms.
• Knowledge of modern software engineering, DevSecOps, CI/CD, and cloud-native architectures.

Leadership & Communication

• Strong executive communication and stakeholder management skills.
• Ability to translate emerging technologies into practical enterprise strategies.
• Proven ability to lead through influence across business and technology organizations.
• Demonstrated thought leadership in data, AI, or enterprise architecture domains.

Preferred Candidate Profile :

A strategic technology leader with deep expertise in enterprise data architecture and a passion for advancing AI adoption. This individual combines strong technical vision, architectural leadership, and business acumen to help the organization build a trusted, AI-ready data foundation while transforming the way engineering teams work through Agentic AI.