Role Overview

As the Head of AI Strategy & Product, you will be the lead architect of the organization’s "Autonomous Reasoning Layer." This is a high-impact, cross-functional role that combines Strategic Management Consulting, Financial Engineering, and AI Systems Design.

You will not just build bots; you will identify, prioritize, and secure funding for the high-value use-cases that redefine our competitive advantage. Your mission is to move the organization from a "Passive" data culture to an "Active" agentic ecosystem where Generative AI (Orchestration) and Classical AI (Machine Learning) collaborate to drive multi-million dollar business outcomes.

Location : Kuwait/Dubai

Key Responsibilities

1. Use-Case Discovery & Value Consulting (The Consultant)

• Portfolio Strategy: Act as an internal consultant to Business Unit leaders, mapping "Logic Workflows" where human reasoning and massive data complexity collide.


• Predictive-Agentic Mapping: Identify opportunities where Classical ML outputs (e.g., Lead Scoring, Demand Forecasts, Churn Propensity) can be utilized by Multi-Agent "War Rooms" to trigger immediate action.


• Prioritization: Develop a proprietary framework (e.g., RICE or Impact vs. Feasibility) to evaluate technical readiness vs. business value, ensuring the team works on the most impactful "Agentic Alpha" projects.

2. Business Case & Funding (The Value Engineer)

• Capital Securitization: Partner with Finance and the CFO to build robust ROI models for AI initiatives, moving beyond "efficiency" to "value creation" metrics like margin expansion and churn reduction.


• TCO Management: Define the Total Cost of Ownership for agentic stacks, including LLM token consumption, Snowflake compute (SPCS/Cortex), and specialized solver licensing.


• Funding Pitches: Present priority initiatives to the Executive Steering Committee, translating complex technical architectures into compelling financial narratives to secure resource allocation.

3. Systems Architecture & Product Leadership (The Architect)

• Data Science Integration: Oversee the development of Predictive Models and Causal Inference engines (e.g., Propensity, LTV, Elasticity) that serve as high-fidelity inputs for the agents.


• Orchestration Strategy: Lead the design of multi-agent topologies (e.g., Supervisor/Worker or Peer-to-Peer), defining the interaction between Analyst, Strategist, Critic, and Auditor nodes.


• Stack Integration: Oversee the synergy between the Intelligence Layer (ThoughtSpot Spotter), the Data Layer (Snowflake), and the Action Layer (Boomi or middleware).


• Agent-Model Interaction: Define how Agents "consume" ML model outputs, deciding when to use a Snowflake Cortex ML function versus a custom-hosted XGBoost or PyTorch model.


• Reflective Logic: Design "Reflection Loops" and "Debate Cycles" to ensure AI decisions are self-correcting and high-quality.

4. Governance & Performance (The Enforcer)

• Risk & Guardrails: Define the "Auditor" constraints and human-in-the-loop (HITL) triggers to ensure the system operates within legal, ethical, and brand boundaries.


• Auditability: Own the "Reasoning Trace" in platforms like LangSmith to explain to stakeholders why agents made specific decisions during complex multi-step processes.


• Feedback Loops: Implement mechanisms where the results of an Agent's action are fed back to retrain and improve the underlying Classical ML models.

Ideal Candidate Profile

Experience & Background

• Professional Pedigree: 8+ years of experience in Management Consulting (MBB/Big 4), Quantitative Finance, or Senior AI Product Management.


• Technology Agnostic Orchestration: Deep conceptual understanding of multi-agent frameworks. Experience with LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom-built state machines is highly valued.


• Modern Data Stack Mastery: Proven experience working within the Snowflake ecosystem (Cortex, Snowpark Container Services) and utilizing ThoughtSpot Spotter for Natural Language to Insight (NL2I) agentic analysis.


• Mathematical Literacy: Comfortable discussing Mathematical Optimization (Linear/Integer Programming) and how to "instrument" these solvers as tools within an agentic graph.

Core Competencies

• The "Translator": Ability to explain a non-linear "State Graph" to a non-technical CEO while debating Python-based constraint logic with a Data Engineer.


• Financial Modeling: Expert-level ability to build NPV/ROI models for transformational technology spend.


• Systems Thinking: The ability to see business processes as a series of states, transitions, and feedback loops.

The "Agentic War Room" Vision

1. Analyst (ThoughtSpot Spotter + Predictive ML): Establishes the ground truth and predicts the statistical future.


2. Scout: Gathers external market context and sentiment.


3. Strategist: Formulates the plan using Causal Inference and instruments the Mathematical Optimizer.


4. Critic: Uses Scenario Simulation and Monte Carlo Stress Testing to challenge the plan's resilience.


5. Auditor: Ensures the final recommendation is safe, compliant, and profitable.


6. Supervisor: Manages the orchestration flow within the Snowflake environment.