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 (Po C 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.

Technical Background: Strong background as a Senior AI/ML Engineer, LLMOps Engineer, or Software Architect who has successfully built and scaled AI-powered applications in enterprise Saa S or complex data platforms.

AI & Engineering Mastery: Deep technical expertise in Python or Go, semantic search, vector databases (e.g., Pinecone, Milvus, pgvector), orchestration frameworks (Lang Chain, Llama Index), and fine-tuning or prompt engineering of state-of-the-art Large Language Models (LLMs).

Extreme Ownership: High-agency mindset. You don’t wait for product teams to ask for AI capabilities; you proactively build the frameworks that solve their bottlenecks before they even identify them.

Software Engineering Rigor: You treat AI development as software engineering. You understand CI/CD, unit testing for AI (evaluation datasets), containerization (Docker/Kubernetes), and clean code architecture.

Influence Without Authority: Exceptional leadership and communication skills. You can inspire and align disparate engineering teams around a shared technical vision without being their direct line manager.

Communication: Exceptional verbal and written English communication skills. Ability to demystify complex AI anomalies or architectures into clear business value for internal stakeholders and executives.