Sr. IT Manager - AI Agentic & Middleware

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.