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Overview
The Product Manager for AI Agents is responsible for ensuring the AI Agent product achieves product-market fit and can be successfully sold, positioned, and scaled across ibex.
This role serves as the commercial owner of the product, bringing together market intelligence, partner capabilities, client feedback, competitive insights, solution messaging, ROI models, sales enablement, and knowledge management into a single commercialization strategy.
The Product Manager works across Product Partners, Sales, Marketing, Wave iX Solutions, Operations, and Technology to ensure the product evolves in response to both market demand and operational realities. This role is also the primary commercial counterpart to the platform partners’ account teams (Account Executive and Solutions Engineering), responsible for translating the partners’ product roadmaps and proof points into ibex-branded commercial assets, and for identifying co-selling opportunities.
This role does not own engineering, implementation, or delivery. Instead, it supports the SVP, Wave iX Commercial in ensuring that the commercial organization always knows what to sell, why clients buy it, how to differentiate it, and how to position ibex as the enterprise AI operating partner. Responsibilities
- Commercial Strategy - own the commercial strategy for the AI Agent product at ibex.
Responsibilities include:
- Monitor AI Agent market trends
- Identify emerging customer needs
- Evaluate competitive positioning
- Recommend new commercial offerings
- Define target industries and use cases
- Maintain the commercial roadmap for AI Agents
Maintain and expand the vertical playbook (currently five verticals: software/high-tech, healthcare, fintech, retail/e-commerce, telco) in partnership with the platform partner's account team, including new verticals as demand emerges
- Market Intelligence - become ibex's internal expert on the AI Agent marketplace.
Includes:
- Competitor analysis
- Platform product comparisons
- Industry trends
- Analyst research
- Client feedback
- Win/loss analysis
Outputs include:
- Quarterly market updates
- Competitive battlecards to educate internal sellers
- Executive briefs
- Opportunity assessments
- Use Case Development - develop repeatable AI Agent use cases.
Examples:
- Conversational IVR, replacing press menu IVRs
- Call spikes
- Seasonality
- Low-complexity, high-volume interactions
- Agent-facing copilot for Internal Knowledge lookup
- Multi-channel identity and account unification
Each use case includes:
- Business challenge, AI solution, Client value, ROI, KPIs, Reference architecture, Required integrations, Success stories
- Each use case should reference existing proof points (e.g. cost reduction and resolution-rate benchmarks from live customer deployments) so reps are not building ROI narratives from scratch per deal.
- Value Proposition & Messaging - own product messaging.
Develop / maintain:
- Value propositions
- Elevator pitches
- Executive presentations
- Differentiators
- Objection handling
- Industry messaging
- Partner positioning
Ensure consistent messaging across Sales, Marketing, Client Success, and Executive Leadership. Maintain a clear internal delineation between white label-safe messaging (client-facing, no vendor names) and partner-facing technical detail (internal only), ensuring reps never inadvertently disclose the underlying platform partnership in client conversations, proposals, or RFP responses. Own the objection-handling FAQ as a living asset, continuously updated rather than treated as a one-time deliverable.
- Sales Enablement - create commercial assets that accelerate revenue.
Examples:
- Sales playbooks
- Discovery guides
- Qualification guides
- ROI calculators
- Proposal content
- Battlecards
- Demo scripts
- Executive presentations
- Customer references
Measure adoption and continuously improve these assets. Own and continuously refine the demo sequencing playbook based on live deal feedback (e.g. leading with lower-risk, non-revenue intents before higher-stakes automation, per lessons learned from active deals). Maintain a standing ROI calculator template (cost-per-ticket, automation rate, resolution fee structure) so reps are not building bespoke models per prospect.
- Partner Commercial Management - work with strategic technology partners including AI platform providers.
Responsibilities include:
- Joint GTM planning
- Co-selling programs
- Sales enablement
- Executive relationship support
- Marketing coordination
- Partner business reviews
- Pipeline development – ibex pipeline and hold a standing joint pipeline review with the platform partner's account team, at minimum monthly, covering active deals in the channel, deal stage, and enablement gaps
- Serve as the primary escalation point for deal-specific support requests from partners (e.g. custom demos, ROI model builds), rather than routing these ad hoc through individual account relationships.
- Coordinate closely with internal teams for technical and operational partner management.
- Knowledge Management - own the commercial knowledge repository.
Includes:
- Use cases
- Success stories
- Win themes
- ROI benchmarks
- Reference architectures (commercial view)
- Proposal language
- Best practices
- Lessons learned
- Contract templates
Ensure knowledge is searchable, reusable, and continuously updated. Build this repository to extend existing collateral already in place rather than duplicate it in a new system. Refresh ROI benchmarks quarterly directly from the platform partner's evolving proof-point set, since resolution rate, cost reduction, and time-to-production figures update as new customers go live.
- Cross-Functional Coordination - serve as the commercial hub across:
- Sales Enablement
- Marketing
- Wave iX Solutions
- Operations
- Technology
- Strategic Partners
Ensure commercial commitments remain aligned with delivery capabilities, including the platform partner's Solutions Engineering team by name, for deal-specific technical scoping (e.g. custom demo builds, POC frameworks) Qualifications
- 7–10+ years in enterprise software, AI, SaaS, consulting, or CX technology
- Experience with conversational AI, virtual agents, contact center technologies, or customer experience transformation
- Strong understanding of enterprise sales cycles
- Experience creating GTM strategies and sales enablement
- Ability to translate technical capabilities into business outcomes
- Executive presentation and storytelling skills
- Experience working across product, engineering, sales, and operations
Core Competencies
- Commercial strategy
- Product marketing
- AI and CX domain expertise
- Executive communication
- Partner management
- Financial and ROI modeling
- Market research
- Cross-functional leadership
- Knowledge management
- Strategic thinking
Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 9,653 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Ibex Limited, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills in Demand for This Role
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $214,400 based on 180 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Ibex Limited AI Hiring
Ibex Limited has 1 open AI role right now. They're hiring across AI Agent Developer. Based in Washington, DC, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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