Associate Director / Director, Data and AI Solution Development

Kardigan – Associate Director / Director, Data and AI Solution Development

The Associate Director / Director, Data and AI Solution Development is a solution-design leader responsible for defining and shaping Kardigan's enterprise data and AI solutions — translating business, scientific, and operational needs into well-architected solution designs, data models, and integration patterns. This role owns solution architecture, requirements analysis, and design for initiatives spanning data products, enterprise data management, and AI-enabled applications, and partners closely with the Solution Delivery team, which owns build and deployment, and the Data Platform Engineering team, which owns the underlying platform's build and operations.

A core focus of this role is designing solutions that make enterprise data usable as high-quality context for human and AI — defining the requirements and designs that ensure data feeding LLMs, RAG systems, analytics copilots, and other AI/ML applications is accurate, well-governed, and discoverable, so that partner teams can build and operationalize it with confidence. More broadly, this role is a driver of Kardigan's technology-enabled transformation, helping the organization innovate through technology and treat data and AI as strategic assets rather than cost centers. They will shape how emerging data and AI capabilities translate into real competitive advantage — accelerating R&D, improving decision-making, and differentiating Kardigan as a modern, data-driven biopharma company.

This individual will operate primarily as a solution architect and designer: setting solution architecture standards, running requirements analysis and design workshops, and partnering with business and scientific stakeholders to shape data and AI solutions. Execution and delivery are owned by a dedicated Solution Delivery team, and platform build/operations are owned by Data Platform Engineering; this role is the design authority that bridges the two, reporting to the Executive Director overseeing Data, AI & Technology and serving as a strategic peer to Data Science/Analytics and AI leadership.

Enterprise Solution Architecture & Design

• Lead solution architecture and design for enterprise data and AI initiatives — translating business and scientific requirements into technical solution designs, data models, and integration patterns
• Define solution patterns, reference architectures, and reusable templates for data and AI solutions that the Solution Delivery team and Data Platform Engineering can build from
• Own requirements definition and business analysis for complex, cross-functional data and AI initiatives, partnering closely with business and scientific stakeholders to clarify and document needs
• Develop proof-of-concepts, prototypes, and solution blueprints to validate approaches and de-risk designs before handoff to the Solution Delivery team; Define acceptance criteria and solution success measures that the Solution Delivery team uses to validate what is built
• Own and prioritize the enterprise data and AI solutions roadmap and intake process, in partnership with business, scientific, and technology stakeholders

Data & AI Solution Foundations

• Partner with the Data Platform Engineering team and Enterprise Architecture team to define the data, API, and platform requirements needed to support new solution designs, including data APIs, semantic interfaces, and access patterns for both human users and AI agents
• Define solution-level requirements for the semantic layer — governed metric definitions, lineage, and natural-language access patterns — so that human users and AI agents can reliably consume enterprise data
• Advise on where the enterprise data warehouse/lakehouse, orchestration layers, and streaming infrastructure need to evolve to support new solution designs, partnering with the platform engineering function that owns their build and operation
• Define requirements for low-latency and near-real-time data access needed to support time-sensitive solution use cases
• Evaluate emerging data and AI tooling (e.g., MCP servers, vector databases, low-code/no-code platforms) for fit with upcoming solution designs, in partnership with Data Platform Engineering

Enterprise Data Management

• Define enterprise data management requirements and standards — data governance, master data management (MDM), metadata management, cataloging, and lineage
• Define and enforce enterprise data standards, taxonomies, and ontologies that drive data quality, consistency, and discoverability across solutions
• Partner with functional data owners (R&D, Clinical, Commercial, Finance, G&A) and data stewardship programs to ensure solution designs reflect accountability for data quality and integrity
• Ensure solution designs comply with applicable regulatory and public-company requirements, including FDA 21 CFR Part 11, data integrity (ALCOA+) principles, GDPR/HIPAA as applicable, and SOX-relevant data controls appropriate for a newly public company
• Partner with Legal, Regulatory Affairs, Quality, and Internal Audit to embed data integrity, privacy, and access-control requirements into every data and AI solution design

Data Products & Solutions-as-a-Service Design

• Define the vision and design for enterprise data products — curated, documented, reusable datasets and APIs — delivered as a service to business, scientific, and technology teams
• Design the solution and data product intake model (SLAs, catalog structure, self-service access patterns) that the Solution Delivery team operationalizes
• Partner with business and scientific stakeholders to translate requirements into well-architected data and AI solution designs, dashboards, and analytics-ready data specifications
• Define solution and data product quality, adoption, and value metrics, and use them to continuously refine solution designs

Agentic Analytics & AI-Facing Solution Design

• Design solutions that enable AI agents to draft queries, detect anomalies, generate insights, and surface recommendations — shifting the organization from dashboard review toward proactive, AI-assisted intelligence
• Define solution designs for AI/ML workloads — including model training data, feature serving, and RAG retrieval — not only dashboards and reports
• Apply hands-on understanding of how LLMs and AI agents consume data — semantic layers, embeddings, vector search, and tool-use patterns (MCP, function calling) — to solution design decisions
• Evaluate and recommend AI-assisted tooling (e.g., automated data quality checks, natural-language-to-SQL) for incorporation into future solution designs

Preparing Data as Context for AI Solutions

• Design the data foundation that enables enterprise AI solutions — including RAG, AI agents, and LLM applications — defining requirements that ensure source data is clean, current, well-structured, and appropriately governed before it is used as AI context
• Architect knowledge base, vector store/embeddings, and semantic layer designs that make enterprise data reliably retrievable and usable by AI-enabled solutions
• Partner with the AI/data science function to define requirements for data pipelines that feed AI/ML models and generative AI solutions, including requirements for monitoring data drift, freshness, and quality