AI Enablement Lead [gn] Data Intelligence

Internal AI tooling: Design and maintain the core AI orchestration layers, centralized API gateways, and reusable frameworks (e.g., advanced RAG architectures, agentic frameworks) for company-wide consumption.

Product AI Integration: Collaborate directly with core engineering teams to embed production-ready generative AI and machine learning features into the Actian Data Intelligence Platform.

LLMOps & Governance Infrastructure: Establish strict guardrails, evaluation frameworks, and monitoring tools to track model performance, bias, data privacy, and security across all AI implementations.

Cost & Latency Optimization: Actively monitor and manage cloud and API compute spend (token management, open-source vs. commercial models) and optimize execution latency for production AI features.

Cross-Functional Upskilling: Lead workshops, design blueprints, and create documentation to empower non-AI engineering teams to build and maintain their own AI-driven features confidently.

Rapid Prototyping (PoC to Production): Drive the engineering execution of high-impact AI proof-of-concepts, ensuring they are built with production-grade code that scales seamlessly.

Standardization of Tooling: Define and enforce the organization's official AI stack, from vector database selection and vector embeddings strategies to semantic caching mechanisms.

Vendor & Open-Source Strategy: Evaluate and manage partnerships with AI model providers and lead the technical assessment of cutting-edge open-source models to keep Actian at the vanguard of innovation.

Data-Driven Impact Tracking: Define and track operational metrics for the AI Enablement function, such as developer adoption rates, reduction in time-to-market for AI features, and ROI of implemented AI tools.