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About This Role
PlanITROI is hiring a proven, hands-on AI Agent builder who specializes in B2B lead generation.
You must have personally architected, built, deployed, and operated AI agents that generate measurable B2B prospecting results.
We're seeking an AI engineer who can build the intelligence behind the system
AI agents that use tools, make decisions, coordinate workflows, and turn enterprise buying signals into qualified sales meetings.
If you are an expert at building AI agents for B2B lead generation and can prove they work, we want to meet you.
About PlanITROI
25 years of success. Global industry recognition. Founder-led. No corporate red tape. An ambitious future powered by AI and Robotics!
For more than 25 years, PlanITROI has helped enterprise organizations maximize the financial value of retired technology through IT lifecycle management, repair, refurbishment, redeployment, remarketing, reverse logistics, and secure IT Asset Disposition (ITAD). PlanITROI is recognized in Gartner's ITAD Market Guide, is an IAITAM Platinum Sponsor, and has built our business around reuse-as-intended-use, prioritizing reuse over recycling.
Our mission: Maximize the financial value of technology while extending its useful life. Every device we recover creates value for clients, reduces unnecessary electronic waste, and gives technology a second life as affordable technology.
Why you'll love working here
Founder-led. Move fast. Get things done. Work directly with entrepreneurial leadership. No layers of corporate bureaucracy or endless approval cycles. Great ideas get heard, tested, and implemented.
Built like an AI startup, backed by an established company. Bring your engineering ideas to a business with 25 years of enterprise relationships, operating experience, and real market opportunities.
Engineer something big. Build AI agents capable of researching 5,000+ enterprise accounts, identifying buying signals, reaching 250,000+ potential right contacts, and generating qualified meetings at scale.
See measurable results. Your agents will directly influence enterprise revenue and help more technology get repaired, reused, and returned to productive use.
Work with people who value builders. We believe in optimism, discipline, humility, ownership, continuous improvement, and recognizing teammates who help others succeed.
Your mission: Build an intelligent AI-powered lead generation engine
Design, build, deploy, and continuously improve an interconnected system of specialized AI agents that identifies enterprise buying opportunities and converts them into qualified meetings. You will own the technical outbound engine end to end, from buying-signal detection and account intelligence to contact discovery, multichannel orchestration, CRM integration, experimentation, and measurement. Our target market (ICP) includes enterprise organizations with 5,000 to 1,000,000+ employees, 80%+ mostly knowledge workers with complex buying committees, and existing technology providers. Your challenge is to find the right buyer, recognize the right moment, and create a compelling reason to engage in PlanITROI.
This role reports to the Founder & CEO and the Chief Strategy Officer. You'll work directly with them and the VP of Marketing, with no layers in between.
Your goal: Qualified enterprise meetings generated and MSA's signed!
What you will build and own
- AI Intent Signal Detection Agents. Identify, ingest, evaluate, and score buying signals, including technology refresh cycles, leadership changes, office moves, M&A, RFP activity, contract timing, job postings, technology initiatives, and social activity. Trigger the right next action.
- AI Buying Committee Intelligence Agents. Identify high-value ICP accounts, research enterprise organizations, map buying committees, select the best 1 to 5 contacts for each entry point, validate contact information, and continuously track changes.
- AI Personalization & Challenger Messaging Agents. Combine account intelligence, buying signals, persona-specific pain points, account history, and incumbent-provider context to create relevant, personalized outreach at scale.
- AI Multichannel Orchestration Agents. Coordinate email, LinkedIn, voicemail drops, AI calling where appropriate, and human SDR/BDR follow-up. Manage sequencing, timing, channel rules, routing, and next-best actions.
- AI Objection Handling & Follow-Up Agents. Recognize and respond intelligently to objections such as "We already have a provider." Maintain conversational context, determine appropriate follow-up, and escalate to humans when needed.
- AI Performance & Optimization Agents. Measure results across signals, personas, channels, and campaigns. Identify winning and losing hypotheses, recommend experiments, and improve qualified meeting conversion.
These are functional capabilities, not a requirement to deploy six separate agents. You will design the architecture, build the agents, integrate the systems, and make them work together.
Non-negotiable technical requirements: This role requires a hands-on builder. You must design, build, and demo production AI agents yourself. If you qualify, we'll give you a real project to prove it. You'll integrate tools like Salesforce, Apollo, and Clay. Using them isn't the role; building the agents that run on top of them is.
We are seeking proven hands-on expertise in:
- AI agent engineering: LLMs, tool calling, reasoning workflows, agent memory, state management, multi-agent orchestration, evaluation, and human-in-the-loop controls.
- Agent frameworks: LangGraph, CrewAI, OpenAI Agents SDK, or comparable frameworks.
- Software development: Python, JavaScript/TypeScript, APIs, webhooks, custom integrations, and automation architecture.
- GTM integrations: Salesforce, Apollo, ZoomInfo, Clay, LinkedIn Sales Navigator, 6sense, Bombora-type intent sources, and AI calling/voicemail platforms.
- Enterprise prospecting: Intent signals, ICP scoring, buying-committee identification, contact resolution, enrichment, personalization, objection handling, and multichannel sequence design.
- Production operations: Agent monitoring, debugging, data quality, CRM synchronization, deduplication, deliverability, compliance, error handling, and auditability.
- Scale: Engineering reliable systems capable of very high outbound volumes, reaching 250,000+ contacts across email, LinkedIn, and voicemail, while protecting platform limits, accuracy, compliance, and deliverability.
You must understand both the technical engineering and the B2B prospecting strategy required to turn agents into a functioning revenue system.
What success looks like
- Qualified meetings generated: Your primary business outcome. Meetings must satisfy defined ICP, persona, and business-need criteria.
- Signal-to-meeting conversion: How effectively your agents convert meaningful buying signals into conversations and qualified meetings.
- Contact Precision: Reaching the right decision-makers and buying committees.
- Agent deployment velocity: How quickly you turn a campaign idea into a reliable, working agent and live test.
- Multichannel conversion: Positive responses, conversations, qualified meetings, and opportunities by signal, persona, channel, and campaign.
- System reliability: Data accuracy, deliverability, CRM integrity, routing, compliance, and scalability.
- Learning rate: How quickly do you identify what isn't working, fix root causes, and scale what produces results.
Our hiring process: Show me, don't tell me
We hire proof, not interviews alone. We're not interested in theoretical AI expertise or polished presentations about what agents might do.
The process has three stages:
- Live demo. Show us AI agents you personally built and deployed for B2B lead generation, and the results they produced.
- Build a project. Build an outbound agent from a campaign premise we give you, using your own tools.
- Live technical session. Demo your build, work a live account, and walk us through your architecture.
Across these stages, be ready to show:
- Your agent architecture, decision logic, prompts, tools, data integrations, and orchestration.
- How your agents identify opportunities and execute prospecting workflows.
- How do your agents handle exceptions, incomplete data, failures, and human intervention?
- Real performance data: qualified meetings, pipelines, conversions, or measurable improvements your systems produced.
Ready for the challenge?
If you love solving difficult technical problems and want to build something ambitious, PlanITROI is where you can make an impact. Bring your engineering expertise, working agents, ideas, and your drive to succeed. We'll bring 25 years of industry experience, enterprise relationships, a substantial market opportunity, and the ability to move fast.
Build the agents. Own the results. Help us grow.
PlanITROI offers a comprehensive benefits package, including health insurance, a 401(k) with company match, paid time off, and more. We are an Equal Opportunity Employer and value diversity at all levels of the organization.
Learn more: PlanITROI.com | NextraTech.com | DigitalDreamsProject.com
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 9,079 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At PlanITROI, Inc., 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 Required
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 $215,000 based on 203 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
PlanITROI, Inc. AI Hiring
PlanITROI, Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Based in Boca Raton, FL, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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