Sr. IT Manager - AI Agentic & Middleware at Alghanim Industries

, , Kuwait -

Full Time

Start Date

Immediate

Expiry Date

27 Jun, 26

Salary

0.0

Posted On

29 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Strategy, Product Management, Strategic Management Consulting, Financial Engineering, AI Systems Design, Generative AI, Machine Learning, Use-Case Discovery, Portfolio Strategy, Predictive-Agentic Mapping, ROI Modeling, TCO Management, Multi-Agent Topologies, Snowflake Ecosystem, LangGraph, ThoughtSpot Spotter

Industry

Retail

Description

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 Analyst (ThoughtSpot Spotter + Predictive ML): Establishes the ground truth and predicts the statistical future. Scout: Gathers external market context and sentiment. Strategist: Formulates the plan using Causal Inference and instruments the Mathematical Optimizer. Critic: Uses Scenario Simulation and Monte Carlo Stress Testing to challenge the plan's resilience. Auditor: Ensures the final recommendation is safe, compliant, and profitable. Supervisor: Manages the orchestration flow within the Snowflake environment.

Responsibilities

This role involves leading the organization's AI strategy by acting as an internal consultant to identify, prioritize, and secure funding for high-value agentic use cases. The leader will oversee the design and integration of multi-agent systems, ensuring collaboration between Generative AI and Classical AI to drive significant business outcomes.