AVP AI and Data Engineering – Employee Benefits

AVP AI and Data Engineering – Employee Benefits

Requirements

• Bachelor’s or master’s degree in computer science, Data Science, Engineering, or a related field.
• 15+ years in data engineering, designing, and developing large-scale data ecosystems.
• A minimum of 3 years of experience in progressively responsible leadership roles (through to the senior management level), managing medium to large-sized teams and cross-functional initiatives.
• Excellent presentation and storytelling skills to communicate complex technical concepts to both technical and non-technical audiences.
• Mastery level Data Engineering and Architecture skills – a deep understanding of data architecture patterns, data warehouse, integration, data lake, data domains, data products, BI, and cloud technology capabilities.
• Experience integrating ML solutions with cloud platforms like AWS SageMaker, GCP Vertex AI and leveraging their pre-built capabilities.
• Technical expertise in: Large Language Models (LLMs) and Generative AI platforms (Anthropic, OpenAI), Prompt engineering and LLM optimization techniques, Retrieval-Augmented Generation (RAG) architectures, Vector database implementations (Vertex AI, Postgres, OpenSearch, Pinecone etc.), AI Agent development and orchestration, Enterprise API development and integration.
• Experience handling model hallucinations, experience with grounding and ranking APIs.
• Experience with GCP, Cloud AI, Vertex AI, and Big Query required.
• Hands-on experience in Lang chain and building AI agents.
• Experience in Vertex AI agent builder and Google Agent space.
• Knowledge in building hybrid data lake-houses involving more than one cloud vendor partner.
• Strong communication skills to describe and explain complex AI/ML concepts and models to business leaders.
• Strong understanding of traditional machine learning algorithms and their applications.
• Expertise in computer vision, including object detection, image segmentation, and image recognition.
• Proficiency in NLP techniques, including sentiment analysis, text generation, and language understanding models. Experience with multimodal language modeling and applications.
• Understanding of Generative AI concepts and LLM Models tailored to a wide variety of automotive applications.
• Hands-on experience with unstructured data mining and content summarization.
• Strong experience with the design and development of complex data ecosystems leveraging next-generation cloud technology stack across AWS or GCP Cloud and Snowflake.

Responsibilities

• Lead the execution of enterprise-wide data strategy, ensuring alignment with AI-driven transformation goals.
• Oversee data architecture standards, develop scalable data consumption patterns, and drive innovation in AI solutions.
• Effectively communicate strategy and progress to diverse stakeholders and promote data capabilities through thought leadership and presentations.
• Create the roadmap and plan to modernize the legacy data platform to strategic cloud platform.
• Solve the current data complexities and enable data products for all consumption archetypes and stakeholders.
• Design and maintain scalable real-time data pipelines for efficient ingestion, processing, and delivery.
• Extract insights from unstructured data and integrate it with structured sources to enable comprehensive analytics.
• Partner with Technology teams to implement a data exchange strategy with Third-Party Administrators (TPAs).
• Build, mentor, and lead a high-performing team of data engineers and architects.
• Identify and champion developer productivity opportunities across the end-to-end data management lifecycle.
• Oversee the design, development, and maintenance of data pipelines, warehouses, and lakes.
• Architect data solutions for AI and machine learning models focused on operational optimization and customer experience enhancement.
• Partner with business stakeholders to translate data needs into technical requirements.
• Stay current with emerging trends in data engineering and AI/ML infrastructure, and recommend innovative tools and technologies to enhance data capabilities.
• Define and implement robust data management frameworks to ensure successful adoption of Enterprise Data Governance and Data Quality practices.
• Manage the budget for the AI Data Engineering function.
• Monitor data pipeline and infrastructure performance, driving continuous improvement and optimization initiatives.

Preferred Qualifications

• Preferred experience in the financial services or insurance industry.