Associate Director / Director, Data & AI Solution Development

Kardigan•Princeton, NJ

5h•$175,000 - $250,000

About The Position

Kardigan is hiring an Associate Director / Director, Data & AI Solution Development to define and shape enterprise data and AI solutions. The role translates business, scientific, and operational needs into solution architectures, data models, integration patterns, and requirements for initiatives across data products, enterprise data management, and AI-enabled applications. It owns solution architecture, requirements analysis, and design, while partnering with the Solution Delivery team, which builds and deploys solutions, and the Data Platform Engineering team, which builds and operates the underlying platform. The role reports to the Executive Director overseeing Data, AI & Technology and serves as a strategic peer to Data Science/Analytics and AI leadership. A central focus is designing enterprise data as accurate, governed, discoverable context for LLMs, RAG systems, analytics copilots, and other AI/ML applications. The role sets solution architecture standards, leads requirements analysis and design workshops, and works with business and scientific stakeholders to shape solutions. It serves as the design authority bridging delivery and platform engineering, helping Kardigan use data and AI as strategic assets to accelerate R&D, improve decision-making, and support competitive differentiation. The position leads enterprise solution architecture and design; defines reusable patterns and reference architectures; owns requirements and solution roadmaps; and develops prototypes and blueprints to validate designs before delivery handoff. It defines solution-level requirements for data platforms, APIs, semantic layers, low-latency access, governance, data products, and AI/ML workloads. It also designs AI-facing data foundations, including knowledge bases, vector stores, embeddings, retrieval, access controls, monitoring, and validation appropriate to a regulated, GxP-adjacent environment. The role unifies solution design across architecture, data management, data products, and AI enablement. It champions requirements discipline, architecture rigor, and reuse; advises on tooling and vendors; defines and reports design-related KPIs/OKRs; and collaborates with IT Operations, Solution Delivery, Data Platform Engineering, Cybersecurity, Enterprise Architecture, and functional stakeholders.

Requirements

Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related quantitative discipline.
Proven record designing enterprise data and AI solutions, including solution architecture, data models, and integration patterns, at enterprise scale within a regulated industry.
Deep technical capability in data architecture, solution design, and cloud platforms, balanced with strategic ability.
Working proficiency in SQL and Python to evaluate and validate solution designs.
Strong business analysis and requirements-definition skills, including translating ambiguous business and scientific needs into technical designs.
Working knowledge of cloud-based data platforms such as Snowflake, Databricks, AWS, Azure, or GCP data services, sufficient to design solutions for partner teams to build and operate.
Demonstrated experience designing data models and integration patterns connecting enterprise systems such as ERP, CRM, LIMS/ELN, and clinical systems to analytics and AI platforms.
Familiarity with modern data-stack components, including warehouse infrastructure, streaming ingestion, semantic/metrics layers, governance and cataloging tools, and orchestration frameworks.
Experience defining data governance, MDM, or metadata management requirements in a regulated or enterprise environment.
Practical experience designing data solutions for AI/ML or generative AI use cases, such as RAG pipelines, feature stores, embeddings, or vector databases.
Demonstrated success partnering with Data Science/Analytics leadership as a peer stakeholder.
Excellent cross-functional communication skills, including presenting solution vision and architecture to executive stakeholders.
Ability to operate effectively in fast-paced, lean, resource-constrained organizations as a hands-on solution architect and designer.
Strong product and solution mindset, treating data and solutions as products and services rather than only projects.
Clear, structured communication skills for explaining technical concepts to engineers and business/scientific stakeholders.
Entrepreneurial mindset, high tolerance for ambiguity, and bias for action.
Strong business acumen connecting data and AI investments to pipeline value, competitive differentiation, and patient outcomes.
Commitment to responsible AI, data ethics, patient privacy, and regulatory compliance.

Nice To Haves

Advanced degree (M.S. or MBA).
5–7 years of experience in data engineering, solution architecture, or enterprise data/AI solution design.
Experience in life sciences, biotechnology, or pharmaceutical environments, including familiarity with clinical, regulatory, or scientific data types.
Familiarity with GxP data integrity requirements, including 21 CFR Part 11 and ALCOA+, and public-company data controls such as SOX.
Experience with data cataloging and governance tools such as Collibra, Alation, Purview, or Unity Catalog.
Hands-on familiarity with generative AI platforms such as Azure OpenAI Service, AWS Bedrock, or Anthropic Claude API, and validated deployment in a regulated environment.
Hands-on understanding of how LLMs and AI agents consume data, including semantic layers, embeddings, vector search, and tool-use patterns such as MCP and function calling.
Track record designing data-as-a-product solutions, including APIs and self-service platforms for internal and functional partners.
Familiarity with enterprise architecture frameworks such as TOGAF and enterprise integration patterns.
Experience partnering with delivery teams using SDLC or Agile/Scrum methodologies to hand off solution designs for execution.

Responsibilities

Lead solution architecture and design for enterprise data and AI initiatives, translating business and scientific needs into technical designs, data models, and integration patterns.
Define solution patterns, reference architectures, and reusable templates for Solution Delivery and Data Platform Engineering teams.
Own requirements definition and business analysis for complex, cross-functional data and AI initiatives, documenting needs with business and scientific stakeholders.
Develop proof-of-concepts, prototypes, and solution blueprints to validate approaches and reduce design risk before delivery handoff.
Define acceptance criteria and solution success measures for delivery teams.
Own and prioritize the enterprise data and AI solutions roadmap and intake process with business, scientific, and technology stakeholders.
Partner with Data Platform Engineering and Enterprise Architecture to define data, API, and platform requirements, including semantic interfaces and access patterns for people and AI agents.
Define semantic-layer requirements, including governed metrics, lineage, and natural-language access patterns.
Advise on the evolution of data warehouse/lakehouse, orchestration, and streaming capabilities to support solution designs.
Define requirements for low-latency and near-real-time data access for time-sensitive use cases.
Evaluate emerging data and AI tools, including MCP servers, vector databases, and low-code/no-code platforms, with Data Platform Engineering.
Define enterprise data management requirements and standards for governance, MDM, metadata, cataloging, and lineage.
Define and enforce enterprise data standards, taxonomies, and ontologies that support data quality, consistency, and discoverability.
Partner with functional data owners and stewardship programs to ensure designs account for data quality and integrity.
Ensure solution designs meet applicable regulatory and public-company requirements, including FDA 21 CFR Part 11, ALCOA+ principles, GDPR/HIPAA as applicable, and relevant SOX controls.
Partner with Legal, Regulatory Affairs, Quality, and Internal Audit to embed data integrity, privacy, and access-control requirements in solution designs.
Define the vision and design for enterprise data products, including curated, documented, reusable datasets and APIs delivered as services.
Design the data product intake model, SLAs, catalog structure, and self-service access patterns for operationalization by Solution Delivery.
Translate stakeholder 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 refine designs.
Design solutions that enable AI agents to draft queries, detect anomalies, generate insights, and surface recommendations.
Define solution designs for AI/ML workloads, including model training data, feature serving, and RAG retrieval.
Apply understanding of LLM and AI agent data consumption—including semantic layers, embeddings, vector search, and tool-use patterns—to design decisions.
Evaluate AI-assisted tools such as automated data quality checks and natural-language-to-SQL for future solution designs.
Design data foundations for RAG, AI agents, and LLM applications, ensuring source data is clean, current, structured, and governed for use as AI context.
Architect knowledge bases, vector stores/embeddings, and semantic layers for reliable enterprise data retrieval and use by AI solutions.
Partner with AI/data science teams on requirements for data pipelines feeding AI/ML and generative AI, including monitoring data drift, freshness, and quality.
Define access control, redaction/masking, and audit logging requirements for data used in AI contexts.
Define documentation and validation requirements for AI data solutions appropriate to regulated, GxP-adjacent environments.
Own and unify the data and AI solution design roadmap across architecture, data management, data products, and AI-enablement workstreams; serve as the primary point of accountability for solution design.
Champion a solution design culture grounded in requirements discipline, architecture rigor, and reusability; advise on vendor and tooling selection with procurement and delivery teams.
Define and report KPIs/OKRs for solution design quality, requirements clarity, reuse, and stakeholder satisfaction.
Collaborate with IT Operations, Solution Delivery, Data Platform Engineering, Cybersecurity, and Enterprise Architecture to ensure designs are secure, integrated, and consistent with the broader IT landscape.