VP IT Management - IM04AE
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Overview of Role
As a VP of AI & Data Architecture, you will lead transformative initiatives to design and build scalable, enterprise-grade AI & Data architecture and platforms that enable agentic frameworks and differentiating data-driven experiences. You will lead the strategy and vision for designing highly scalable, reliable, and secure Enterprise AI & Data platforms, running in a multi-cloud environment. This role will lead cross-functional technical teams to help shape the organization's AI and Data maturity and drive strategy that emphasizes development of core data domains, productizing data and insights, and the integration of AI/ML capabilities enabled by robust platforms.
Primary Job Responsibilities
- Strategic Architecture Leadership: Develop and execute a comprehensive Enterprise AI and Data Architecture strategy, which include Strategic Capability maps, Enterprise data models, and multi-cloud architecture patterns that ensure scalability, reliability, and security.
- Gen AI Data Architecture: Design and implement modern data architectures such as Lakehouse (OTFs) and Lakebase. Design capabilities to support real time streaming analytics, data API integrations, GenAI applications, including structured and unstructured , real time data ingestion/transformation, segmentation and tagging, storage (object store, vector databases, caching), processing, and retrieval for large language models (LLMs), Vision Language Models (VLM) and other AI/ML models. Additionally, enable model prompt tuning, fine-tuning, evaluation, RLHF, optimization and monitoring architecture.
- Data Governance and Privacy: Establish and drive a strategy for Enterprise-wide Data Governance and Data Privacy programs that maximize value from data by producing trusted, high quality data products that are easily discoverable by data consumers.
- Capability Maturation: Drive the creation and methodical application of metrics that assess maturity and demonstrate the progression of AI & Data enabled capabilities across the enterprise.
- Advanced AI & Data Platform Design: Architect and design complex data platforms leveraging Snowflake, AWS, GCP, and other cutting-edge technologies. Design multi-cloud data integration and lifecycle management. Technology Evaluation and Adoption: Perform ongoing market scans for emerging solutions, technologies, and architectural concepts to incorporate into an evolving portfolio of data and AI capabilities.
- Technology Leadership: Provide technical leadership and guidance on AI & Data architecture best practices, including data governance, data security, and data integration.
- Elevate the Community: Cultivate and lead communities of practice that evangelize the benefits and promote the adoption of re-useable solution patterns, along with establishing a feedback mechanism for continuous improvement and iterations of such patterns.
- Mentorship and Evangelism: Provide leadership and mentorship to federated teams of architects and engineers, fostering a culture of innovation, collaboration, and continuous upskilling and learning.
- Documentation: Oversee the creation and maintenance of comprehensive documentation of AI and Data architecture designs, standards, and best practices.
- Stay Current: Continuously evaluate and recommend new data technologies and trends to improve our data capabilities.
Skills
- Expert understanding and hands on experience with multiple cloud platforms (e.g., AWS, GCP, Snowflake).
- Knowledge of leading data integration and transformation tooling. (real-time data streaming, agentic frameworks, Data APIs, vector stores, and RAG architectures, self-serve analytics and AI.)
- Ability to lead large scale transformation initiatives.
- Excellent communication, presentation, and leadership skills.
- Ability to influence and collaborate with Executive leadership.
- Experience with advanced AI/ML and Agentic and data pipelines.
- Deep understanding of AI/ML and Data software engineering principles and operational best practices using open source, cloud technologies, modern platform tools, data pipelines, lakehouse architetcures (OTF), enterprise data warehousing, and large-scale data transformations, pre-training, post-training, evaluation, serving, and monitoring. Knowledge of DevOps, DataOps, MLOps, LLMOps, and AIOps.
- Ownership mentality, taking accountability for project success and the continuous improvement of data engineering processes, architecture, and operating models.
Education, Experience, Certifications and Licenses
- Bachelor’s or Master’s degree in Computer Science, Information Systems, a related field, or equivalent work experience.
- 12+ years of experience in data architecture, with a focus on enterprise-level data solutions.
- 6 + years of experience in AI/ML Architecture
- 10+ years of experience of people leadership (formal or matrix).
- Deep expertise in designing and implementing data and system architectures for AI/ML, Generative AI, and Agentic applications.
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$222,480 - $333,720
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
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