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Introduction

At IBM, we believe technology shapes the world. We’re a catalyst for that innovation. We’re driving change that improves businesses, society, and the human experience. Our Marketing, Communications & Corporate Social Responsibility (MCC) team tells this story. We shape IBM’s brand, capture attention in the market, and share our perspective with clients, partners, the media, and fellow IBMers. On our team, you’ll work with bright, collaborative minds who bring passion and creativity to everything they do. You’ll be part of a culture built on openness, trust, and teamwork. Where your ideas matter and your growth is supported. Join us, and help bring innovation to life.

Your role and responsibilities

We are building a small, elite AI strike team embedded in the Marketing, Communications, and CSR (MCC) Strategy & Operations team. The mission is to move fast, explore what’s possible with agentic and conversational AI, and prove value quickly—before anything is scaled or productionized elsewhere.

This role leads that squad. You will be a hands‑on AI leader with deep expertise in Large Language Models, conversational AI, and agent design. You will guide rapid experiments, build compelling proof‑of‑concepts, and help senior marketing leaders understand how AI can meaningfully change how work gets done.

This is not a traditional AI platform role, a reporting function, or a research lab. It is a high‑judgment, rapid‑execution role optimized for learning, momentum, and decision‑making. (editado)

This Role IS
• A hands‑on leader for a small, elite AI delivery team
• Focused on agentic AI, conversational experiences, and copilots
• Oriented around workflow transformation, not tools for their own sake
• Optimized for speed, experimentation, and proof‑of‑value
• Embedded directly in MCC Strategy & Operations Team
• Closely partnered with senior Marketing, Communications, and CSR leaders

This Role Is NOT
• A dashboarding or reporting function
• A long‑term AI platform or MLOps ownership role
• A research‑only or theory‑heavy AI role
• A large team or program management position

Core Responsibilities
1. Leadership & Execution
• Lead a small, high‑trust AI squad focused on rapid delivery
• Set clear, time‑boxed missions with explicit learning or go/no‑go outcomes
• Balance hands‑on building with technical direction and coaching
• Operate with a “build → test → learn → decide” mindset
• Optimize for progress and insight, not polish or permanence

2. Conversational AI & LLM Expertise (Core Focus)
• Apply deep understanding of LLM capabilities and constraints, including:
• Tokenization and context windows
• Latency and cost tradeoffs
• Failure modes and hallucination risks
• Design multi‑turn, high‑quality conversational experiences
• Manage conversation state, context, and memory deliberately
• Create flows that balance strong UX, deterministic logic, and LLM flexibility

3. Query Understanding, Routing & Agent Selection
• Design systems that translate user input into confident routing decisions
• Apply intent detection, semantic similarity, and hybrid triage approaches
• Decompose complex requests into actionable subtasks
• Select and orchestrate agents based on role, context, confidence, and history
• Build fallback paths, confidence scoring, and uncertainty handling
• Implement guardrails to prevent unsafe, incorrect, or misleading behavior

4. Agent Design & Orchestration Patterns
• Lead hands‑on experimentation with proven agent patterns, including:
• Router / Dispatcher
• Planner–Executor
• ReAct
• Tool‑calling agents
• Retrieval‑Augmented Generation (RAG)
• Make pragmatic choices between prompt‑driven and code‑driven controls
• Optimize for predictable, explainable behavior, not full autonomy
• Design agents that are credible and trusted in an enterprise marketing context

5. Prompt Engineering & Rapid Iteration
• Develop advanced system, developer, and user prompt architectures
• Create prompt templates for routing, planning, classification, and tool use
• Manage prompt versioning and experimentation
• Use constraints, grounding, and structured outputs to reduce hallucinations
• Iterate quickly based on live usage and feedback

6. Lightweight Governance & Enterprise Awareness
• Ensure experiments consider safety, bias, explainability, and privacy
• Apply governance thoughtfully, without slowing experimentation
• Implement basic logging, evaluation, and traceability
• Communicate risk, limitations, and tradeoffs clearly to stakeholders

7. Partnership with MCC Leadership
• Translate marketing strategy and business needs into focused AI experiments
• Partner with senior marketing leaders as a thought partner and guide
• Clearly explain what AI can and cannot do—without hype
• Help leaders decide what to pursue further, what to iterate, and what to stop

Leaders are expected to spend time with their teams and clients and therefore are generally expected to be in the workplace a minimum of three days a week, subject to business needs.

Leaders are expected to spend time with their teams and clients and therefore are generally expected to be in the workplace a minimum of three days a week, subject to business needs.

Required education

Bachelor's Degree

Preferred education

Master's Degree

Required technical and professional expertise

8–12+ years in AI, applied ML, software, data, or applied technology roles

· Deep, hands‑on experience with LLMs and conversational AI

· Strong understanding of agentic workflows and orchestration

· Experience operating in ambiguous, fast‑moving environments

· Experience with conversational AI platforms or orchestration tools

· Exposure to RAG systems, semantic search, or vectorization

· Familiarity with enterprise AI considerations and constraints

· Experience with tooling such as watsonx.ai, watsonx Orchestrate, or similar platforms

Mindset & Ways of Working

· Bias toward action, experimentation, and learning

· Comfortable showing early work and iterating in the open

· Strong communicator with both technical and non‑technical audiences

· Confident making tradeoffs between speed, quality, and risk

· Energized by small teams and high accountability

Preferred technical and professional experience

Mindset & Ways of Working

· Bias toward action, experimentation, and learning

· Comfortable showing early work and iterating in the open

· Strong communicator with both technical and non‑technical audiences

· Confident making tradeoffs between speed, quality, and risk

· Energized by small teams and high accountability