Agentic Lead / Applied AI Prototyper

Agentic Lead / Applied AI Prototyper

Department	New Product Development (NPD)
Reports To	Head of New Product Development
Type	Full-Time
Location	Hybrid

$144,000 - $180,000 + bonus

About IANS Research

IANS Research is the leading resource for information security and technology risk professionals. Our faculty-driven model delivers practitioner-grade advisory, research, and tools that help CISOs and their teams make better decisions faster. With 229 employees and a growing AI-enabled product portfolio, IANS is at an inflection point—investing heavily in agentic AI capabilities that extend and amplify our proprietary IP.

Position Summary

The Agentic Lead / Applied AI Prototyper is a builder role embedded in New Product Development with one clear mission: prototype agentic workflows and AI-generated output formats that are grounded in IANS data and IP, move fast, and generate tangible artifacts the organization can evaluate, test, and scale.

 

This is not a strategy or advisory position. The Agentic Lead spends the majority of their time building—experimenting with Claude and other LLM frameworks, assembling multi-step agentic workflows, and producing concrete outputs (board-ready memos, diagnostics, risk scorecards, structured briefings) that demonstrate what AI-enabled IANS content can look like at its best.

 

Prototypes that demonstrate validated impact and repeatability graduate into the product backlog through a structured handoff process—ensuring that the Studio’s best work becomes durable product capability rather than orphaned experiments.

Key Responsibilities

Agentic Workflow Prototyping

• Design, build, and iterate on multi-step agentic workflows using Claude, LangChain, LlamaIndex, AutoGen, or comparable frameworks—grounded in IANS research content, faculty IP, and proprietary data assets.
• Develop workflow modules for high-priority use cases: AI-assisted meeting prep, automated CRM enrichment, research synthesis, client engagement plan generation, and renewal risk scoring.
• Build and test prompt architectures, retrieval-augmented generation (RAG) pipelines, and structured output templates that produce consistent, high-quality, on-brand IANS artifacts.
• Maintain a rapid iteration cadence—prototype, test with internal stakeholders, discard or refine, and move on. Speed and learning velocity are the primary performance metrics here.

Output Format Development

• Produce working examples of AI-generated IANS output formats: board-ready security briefings, CISO diagnostics, risk scorecards, issue-based memos, and structured Ask-an-Expert summaries.
• Collaborate with faculty members to validate AI-generated artifacts for accuracy, nuance, and practical relevance—ensuring outputs meet the standard clients expect from IANS.
• Document the prompt patterns, data inputs, and workflow logic behind each successful output format so they can be reproduced, taught, and eventually scaled by the Enablement function.

Studio Operations & Knowledge Transfer

• Maintain an active, documented library of prototypes, agentic playbooks, and output templates—organized for eventual handoff to the AI Enablement Lead for broader deployment.
• Participate in structured handoff processes when Studio prototypes are ready for scaling—providing technical documentation, success criteria, and integration notes for the Ask IANS engineering team.
• Share learnings across the Studio, Ask IANS, and Enablement functions—contributing to a shared understanding of what works, what doesn’t, and what’s next.
• Engage directly with the CPO and NPD leadership to align Studio output priorities with organizational strategy.

Technical Exploration & Tooling

• Continuously evaluate emerging LLM capabilities, agentic frameworks, and AI tooling relevant to IANS’s use cases—bringing informed recommendations to the team rather than defaulting to the familiar.
• Assess and integrate new model capabilities (extended context, function calling, structured outputs, multi-agent coordination) as they become available.
• Identify infrastructure or data layer requirements that would unlock higher-quality agentic outputs and collaborate with Ask IANS engineering to address them.

Qualifications

Required

• Hands-on experience building LLM-powered applications or agentic workflows in a production or near-production context—not just experimentation.
• Proficiency in Python and comfort working with LLM APIs (Anthropic Claude, OpenAI, or equivalent) including prompt engineering, function/tool calling, and structured output design.
• Experience with at least one agentic or RAG framework: LangChain, LlamaIndex, AutoGen, CrewAI, or similar.
• Strong instincts for what makes a good AI-generated output—able to evaluate quality, consistency, and fitness-for-purpose without always needing external validation.
• Ability to work in a fast, iterative environment with minimal process overhead—comfortable with ambiguity, able to self-direct, and oriented toward shipping over theorizing.
• Excellent written communication skills; able to document prototypes, playbooks, and workflows clearly enough that others can reproduce and build on them.

Preferred

• Experience working with RAG architectures including vector databases (Pinecone, Weaviate, pgvector, or similar) and embedding pipelines.
• Background in content-heavy or knowledge-intensive domains (research, advisory, legal, financial services) where AI output quality and accuracy are non-negotiable.
• Familiarity with multi-agent architectures and orchestration patterns for complex, multi-step reasoning workflows.
• Experience producing structured, templated AI outputs (reports, scorecards, briefings) for professional audiences.
• Exposure to cybersecurity, information security, or technology risk content—or the ability to learn quickly in a domain-specific environment.
• Understanding of the IANS faculty model and how practitioner-grade advisory content is created, validated, and delivered to clients.

 

IANS Research is an equal opportunity employer.