QData - VP of AI (Innovation & Engineering)

The Opportunity: Architecting Intelligence at Sovereign ScaleQData is the Data and AI execution arm for ADQ. We are building the AI Factory that will power the transformation of ADQ's massive portfolio of Operating Companies (OpCos). We are looking for a VP of AI to lead our AI engineering, innovation, and forward-deployed practices.This is not a theoretical research role. You will sit at the intersection of Innovation (Data Science and Applied AI), AI Engineering, and Forward Deployed Engineering (FDE). Your mandate is to industrialize AI: moving from "vibe-coded" prototypes to resilient, production-grade engines that drive tangible ROI.You will report to the CTO and collaborate closely with the VP of Data, VP of Solutions & Products, VP of Application Engineering, and VP of Commercials to deliver rapid roll-outs of AI MVPs, solutions, and products.Role Mission: The Engine of the QData AI Delivery FactoryAs the VP of AI, you are the AI visionary and operational leader of the QData AI delivery factory. You will build and lead a high-performance team responsible for architecting core AI engines, agentic workflows, and orchestration layers.This role requires a unique balance: you must possess the technical depth to mentor Lead AI Engineers on complex RAG pipelines, while having the business acumen to define ROI-driven strategies for OpCo C-suite stakeholders.Why Join QData?Impact: You will not just build features; you will transform the economy by deploying AI across critical sectors (Energy, Utilities, Healthcare, Logistics) via the ADQ portfolio.Scale: Access to massive datasets and the resources of a sovereign wealth fund environment.Growth: A clear path to C-level leadership in a rapidly expanding organization.Core Responsibilities1. Strategy & Executive Leadership (Future CAIO)Define the technical vision and roadmap for QData's AI capabilities, transitioning us from ad-hoc solutions to a reusable, scalable AI factory platform.Partner with the VP of Commercials and VP of Solutions & Products to align technical innovation with business outcomes and measurable ROI.Act as the primary technical voice for AI across the ADQ portfolio, championing "AI Safety by Design" and governance alongside enterprise risk requirements.2. Leading Innovation & Data ScienceDrive the R&D agenda, staying at the bleeding edge of research (e.g., small language models, context window optimization, new embedding techniques) and translating them into practical QData playbooks.Establish rigorous Evaluation Frameworks (quantitative/qualitative) to measure model performance against business KPIs, ensuring we move beyond "hype" to "value".3. Mastering AI Engineering & Operations (LLMOps)Oversee the architecture of robust LLM orchestration layers and high-performance multi-layer memory systems.Institutionalize LLMOps best practices: automated evaluation (LLM-as-a-judge), monitoring, logging, and versioning for prompts and models.Ensure the engineering team builds distributed systems that maintain low latency under high throughput.4. Forward Deployed Engineering (FDE) ManagementLead the FDE practice, ensuring your teams can "get into the trenches" with OpCos to integrate AI into diverse environments—from legacy on-prem stacks to modern cloud infrastructure.Ensure your team acts as technical partners to the OpCos, navigating technical constraints to deliver MVPs that solve root‐cause business problems.5. Talent & CultureRecruit, mentor, and retain world‐class AI talent, fostering a culture of "Pragmatic Innovation" where engineers choose the effective tool over the theoretical one.Set the "gold standard" for code quality and engineering rigor across the organization.Technical Mastery1. The AI Marketplace & API EcosystemModel-as-a-Service Architecture: Design the technical backbone of the QData AI Marketplace, allowing OpCos to discover and consume pre-configured AI models and agents via secure, standardized APIs.Service Catalog & Governance: Architect the governance layer that controls model access, usage policies, and versioning across the marketplace, ensuring "AI Safety" extends to consumed services.Monetization & Metering: Oversee the implementation of metering and telemetry systems required to track usage for chargeback models, turning the AI Factory into a sustainable revenue center.2. Advanced Cognitive Architectures & Agentic OrchestrationAgentic Workflows: Possess an architectural command of agentic systems, moving beyond simple chains to complex, stateful multi‐agent systems that solve non‐linear problems.Orchestration Layers: Deep experience designing orchestration layers capable of managing complex reasoning loops, tool use, and error handling in production environments.Memory Systems: Expertise in constructing high‐performance memory systems, implementing long‐term persistence strategies that allow agents to retain context across sessions.3. Next‐Gen Information Retrieval (RAG) & Data StrategySophisticated RAG Patterns: Mastery of Hybrid Search (Dense + Sparse), GraphRAG, and re‐ranking strategies to ensure high‐fidelity retrieval from diverse corporate sources (SaaS, On‐prem, Data Lakes).Vector Operations: Proven ability to optimize Vector Stores and embedding models for specific enterprise domains, ensuring data quality and relevance at scale.4. Model Optimization & The "Small Model" RevolutionFine‐Tuning Lifecycle: Deep grasp of Parameter‐Efficient Fine‐Tuning (PEFT/LoRA) to adapt foundation models to proprietary enterprise data without incurring massive compute costs.SLM Operationalization: Ability to operationalize Small Language Models (SLMs) for edge cases or latency‐sensitive tasks, balancing performance trade‐offs against massive frontier models.Context Optimization: Knowledge of context window optimization and token economics to maximize ROI on inference costs.5. Industrial‐Grade LLMOps & EvaluationSystematic AI Engineering: Champion the move from "vibe‐coding" to rigorous engineering. This includes designing Evaluation Frameworks using quantitative and qualitative metrics (including LLM-as-a-judge).CI/CD for AI: Expertise in architecting testing suites that handle the non‐deterministic nature of LLMs to ensure safety and reliability before deployment.Observability: Implementation of comprehensive observability stacks for monitoring drift, hallucination rates, and latency in real‐time.6. Distributed Systems & Core EngineeringCode Fluency: Retain expert‐level literacy in Python and TypeScript/FastAPI ecosystems to conduct high‐level code reviews and architectural audits.Scale: Proven ability to design distributed systems that maintain low latency under high throughput, ensuring AI microservices can scale to meet the demands of 50+ Operating Companies.Candidate ProfileExperience & Leadership10+ years of technical experience, with at least 5+ years in leadership roles managing large size AI engineering or data science teams.Proven track record of deploying AI/ML models in Enterprise or B2B environments, moving beyond notebooks to production‐scale applications, incubating and scaling AI Factory.Experience in a high‐growth startup, consultancy, or "Forward Deployed" environment is critical; you must be comfortable leading teams that face customers directly.Mindset & BehaviorsROI‐Obsessed: You prioritize business impact over research for research's sake.Bias for Action: You drive your team to ship code and solve problems rather than writing whitepapers.Low Ego: You are equally comfortable strategizing with the Board or debugging an architectural bottleneck with a junior engineer.Pragmatic Innovation: You choose the most effective tool for the job—whether a simple script or a complex agentic system.DisclaimerThis job posting is not open to recruitment agencies. 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