AI Lead – Agentic & Conversational AI
IBM Computing, Austin, TX, United States
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 About the Role 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.
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
Leadership & Execution
Lead a small, high‑trust AI squad focused on rapid delivery
Set clear, time‑boxed missions with explicit learning or go
o‑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
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
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
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
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
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
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
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
Equal Opportunity Statement IBM is committed to creating a diverse environment and is proud to be an equal‑opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.
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Your role and responsibilities About the Role 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.
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
Leadership & Execution
Lead a small, high‑trust AI squad focused on rapid delivery
Set clear, time‑boxed missions with explicit learning or go
o‑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
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
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
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
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
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
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
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
Equal Opportunity Statement IBM is committed to creating a diverse environment and is proud to be an equal‑opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.
#J-18808-Ljbffr