Top Tech Jobs & Startup Jobs in Austin, TX

Leads enterprise strategy, architecture, governance, and implementation for semantic layers, knowledge graphs, ontologies, taxonomies, and AI-enabled knowledge capabilities. Partners with AI, data science, product, engineering, and business teams to enable trusted AI, analytics, search, interoperability, and reusable knowledge. Establishes metadata and knowledge lifecycle standards, advances RAG and semantic search innovation, and builds a high-performing team of semantic and knowledge engineers.

Leads the strategy, roadmap, and product organization for NovaOS, an enterprise AI operating system. Oversees a portfolio spanning data, semantic, agentic, and experience layers; partners with business, engineering, and technology leaders to prioritize investments and deliver measurable outcomes. Establishes product governance, lifecycle practices, success metrics, and feedback mechanisms while building and mentoring a high-performing product management team.

Leads enterprise core data management by defining standards for data, metadata, modeling, and quality; driving governance adoption; and delivering trusted, AI-ready datasets. Oversees data operations, service levels, documentation, metrics, risk management, and continuous improvement. Partners with engineering, business teams, vendors, and senior stakeholders while leading and developing Associate Directors and distributed teams.

Leads strategic external data partnerships supporting customer engagement and integrated marketing. Oversees vendor performance, spend, data quality, security, compliance, governance, and integration into enterprise platforms. Establishes SLAs, manages data acquisition and stewardship, resolves quality issues, reports partnership performance to leadership, and coordinates training. Evaluates emerging data offerings, AI-enabled capabilities, and partnership models to improve marketing effectiveness, customer engagement, decision-making, and enterprise value.

Leads the engineering strategy, architecture, development, operations, and continuous improvement of an enterprise agentic AI foundation. Establishes scalable engineering standards, operating models, governance, and reusable platform services that enable AI products and intelligent workflows. Partners with product, data, infrastructure, security, and business teams to modernize existing systems, guide cross-functional delivery, monitor operations, and communicate priorities, risks, and investment needs to senior leaders. Manages and develops product or engineering leaders while driving measurable business value.

Leads enterprise data product strategy, roadmaps, prioritization, adoption, governance, and lifecycle management for reference data, master data, metadata, and standards. Defines reusable, AI-ready products containing business meaning, rules, metrics, lineage, and context. Partners with data enablement, technology, analytics, governance, and business teams to scale reliable products, establish standards and service expectations, measure value, resolve issues, and drive stakeholder adoption.

Coordinates technical onboarding and ongoing operations for data partners and vendors. Ensures data delivery, ingestion, metadata, catalogs, documentation, and inventories remain accurate and current. Tracks data quality, SLAs, delivery performance, operational metrics, and issue resolution across business, technical, engineering, analytics, and vendor teams. Maintains centralized issue trackers, escalates risks, translates technical problems into business impacts, and drives cross-functional follow-through in a matrixed environment.

Leads rapid design, development, and validation of agentic AI proof-of-concepts. Partners with stakeholders, product, architecture, and engineering teams to identify opportunities, build LLM-based prototypes, evaluate emerging technologies, document methodologies, and transition successful solutions into production roadmaps. The role also mentors AI engineers and supports enterprise AI innovation.

Leads the design, prototyping, and validation of AI agents and autonomous workflows. Evaluates foundation models, agent frameworks, orchestration technologies, and developer tools; creates reusable proofs of concept and reference implementations; and partners with engineering, product, platform, and architecture teams to productionize successful innovations. Establishes best practices for prompt engineering, reasoning, memory, evaluation, and observability while mentoring AI engineers and promoting experimentation.

Leads strategy, roadmap, prioritization, adoption, governance, and lifecycle management for customer-facing payer, HCP, omnichannel, and engagement data products. Partners with data enablement, technology, analytics, and business teams to create trusted, reusable, AI-ready products incorporating semantic definitions, metadata, lineage, standards, and measurable business value.