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About This Role
PlanITROI is hiring a hands-on GTM engineer to build and run our AI-powered lead generation engine. You build the core AI agents first, then run the campaigns on them every day, turning enterprise buying signals into qualified sales conversations.
You might have started in outbound, SDR leadership, demand gen, or growth, then taught yourself to build the agents and automations that do the work. Or you're an engineer who learned B2B prospecting the hard way. Either way, you've personally built systems that booked real meetings, and you can show us how you have been successful.
If that's you, we would like to meet you!
About PlanITROI
For more than 25 years, PlanITROI has turned retired technology into financial value for enterprise organizations. Our services cover IT lifecycle management, repair, refurbishment, redeployment, remarketing, reverse logistics, and secure IT Asset Disposition (ITAD).
We operate on reuse-as-intended-use: reuse before recycling, and recycling only when reuse isn't viable. PlanITROI is recognized in Gartner's ITAD Market Guide and is an IAITAM Platinum Sponsor.
Our mission is to maximize the financial value of technology while extending its useful life. Every device we recover creates value for clients and gets a second life as affordable technology.
Your mission
Build and run the AI-powered outbound engine that finds enterprise buying opportunities and turns them into qualified meetings and signed MSAs. Your agents will research 5,000+ enterprise accounts and reach 250,000+ contacts.
You own it end-to-end: signal detection, account intelligence, contact discovery, personalization, multichannel orchestration, CRM integration, experimentation, and measurement.
Build first, then run. You start by building a few core agents. Then you run the campaigns on them: launching and monitoring sequences, working reply queues, tuning messaging and targeting every week, protecting deliverability, and handing warm conversations to sales at the right moment. Expect most of your time to shift from building to running once the agents are live.
Who we sell to. Enterprise organizations with 5,000 to 11,000,000+ employees, mostly knowledge workers (80%+), complex buying committees, and an existing technology provider. Your challenge: find the right buyer, recognize the right moment, and give them a compelling reason to talk to PlanITROI.
Who you work with. You report to the Founder & CEO and the Chief Strategy Officer, and work closely with the VP of Marketing. No layers in between other than support.
What you'll build and own
- Signal detection. Ingest, evaluate, and score buying signals such as tech refresh cycles, leadership changes, office moves, M&A, RFP activity, contract timing, job postings, and social activity. Trigger the right next action while the signal is fresh.
- Buying committee intelligence. Identify high-fit ICP accounts, map buying committees, select the best 1 to 5 contacts per entry point, validate contact data, and track changes.
- Personalization and challenger messaging. Combine account intelligence, signals, persona pain points, account history, and incumbent-provider context into relevant outreach at scale.
- Multichannel orchestration. Run email, LinkedIn, voicemail drops, AI calling where appropriate, and human SDR follow-up as one system, with sequencing, timing, routing, and next-best actions.
- Objection handling and follow-up. Respond intelligently to objections like "We already have a provider," keep conversational context, and escalate to humans at the right moment.
- Performance and optimization. Measure results by signal, persona, channel, and campaign. Find what's winning, kill what isn't, and run the next experiment.
These are capabilities, not six separate agents. You design the architecture, build the agents, integrate the systems, and make them work together.
What you bring
This role requires a hands-on builder. You must design, build, and demo production AI agents yourself. You'll integrate tools like Salesforce, Apollo, and Clay, but using them isn't the role. Building the agents that run on top of them is.
You understand both sides: the engineering, and the B2B prospecting strategy that turns agents into a working revenue system.
Must-haves
- AI agents that booked meetings. You've personally built and run AI agents or automated outbound systems that produced measurable B2B meetings, and you have the performance data to prove it.
- Agent engineering. LLMs, tool calling, reasoning workflows, memory and state, multi-agent orchestration, evaluation, and human-in-the-loop controls, using LangGraph, CrewAI, OpenAI Agents SDK, n8n, or comparable frameworks.
- Coding and integration. Python or JavaScript/TypeScript, APIs, webhooks, and custom integrations.
- Enterprise prospecting. Intent signals, ICP scoring, buying committee mapping, enrichment, personalization, objection handling, and multichannel sequence design. Bonus if you've displaced incumbent providers.
- GTM stack. Hands-on with tools like Salesforce, Apollo, ZoomInfo, Clay, LinkedIn Sales Navigator, 6sense or Bombora, and AI calling or voicemail platforms.
- Production operations at scale. Monitoring, debugging, CRM sync, deduplication, deliverability, compliance, and error handling for systems reaching 250,000+ contacts across email, LinkedIn, and voicemail without burning domains or accounts.
Nice-to-haves
- A background in outbound, SDR/BDR leadership, demand gen, growth, or RevOps before moving into AI and automation.
- Experience in IT services, ITAD, or computer hardware, hardware as a service
What success looks like
Your primary outcome is qualified enterprise meetings that meet defined ICP, persona, and business-need criteria. We also track:
- Signal-to-meeting conversion. How well your agents turn buying signals into conversations and meetings.
- Contact precision. Reaching the right decision-makers and buying committees.
- Deployment velocity. How fast a campaign idea becomes a working agent and a live test.
- Multichannel conversion. Replies, conversations, meetings, and opportunities by signal, persona, channel, and campaign.
- System reliability. Data accuracy, deliverability, CRM integrity, routing, and compliance.
- Learning rate. How quickly you find what isn't working, fix the root cause, and scale what is.
How we hire: show us, don't tell us
We hire on proof, not interviews alone. We're not interested in theoretical AI expertise or polished slides about what agents might do. If you qualify, we'll give you a real project to show us how you build.
- Live demo. Show us AI agents you personally built and deployed for B2B lead generation, and the results they produced.
- Build 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 architecture, decision logic, prompts, tools, and integrations; how your agents handle exceptions, incomplete data, failures, and human handoff; and real performance data such as meetings, pipeline, or conversion gains.
You won't need access to any PlanITROI system or need to buy software. Free tiers and trials are fine. Your work stays yours unless we hire you. If you show work from a past employer, please redact anything confidential.
Ready to build?
If you love hard technical problems and want to build something ambitious, bring your working agents, your results, and your drive.
Build the agents. Own the results. Help us grow.
Explore more about us: PlanITROI.com | NextraTech.com | BuyRefurbished.com
PlanITROI is an equal opportunity employer. We prohibit discrimination/harassment without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At PlanITROI, Inc., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 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/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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