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About This Role
Location: Orlando , FL / Remote / Hybrid] Department: Sales & Operations Reports to: CTO/CSO / Director of Operations
About Iserv
At Iserv, we are redefining what it means to be a Managed Service Provider. We don't just fix computers; we empower businesses to navigate the future of work. By combining robust IT infrastructure with the transformative power of Artificial Intelligence, we help our clients secure their data, automate their workflows, and gain a competitive edge. We are an "AI-First" company, meaning we practice what we preach—constantly looking for smarter, faster ways to solve problems.
The Role
We are looking for a Technical Client Success Manager who acts as the strategic "hub" of our organization. You will sit at the intersection of Sales, Project Management, and Client Success.
This role requires more than just organization; it requires innovation. We need a multitasker who stays cool under pressure but also possesses a genuine passion for AI and automation. You will be the one ensuring our clients feel supported while simultaneously using the latest tech to streamline how that support happens.
If you are the type of person who sees a repetitive task and thinks, "How can I automate this?" or "How can AI help me organize this data?"—you are the independent thinker we need.
Key Responsibilities
1. Client Relationship & "Future-Proofing"
- Serve as the primary liaison for key accounts, ensuring they aren't just "happy," but are successfully adopting the technology we deploy.
- Identify opportunities within existing client accounts where AI or Automation solutions could solve a business pain point, and tee these up for the Sales team.
- Conduct quarterly business reviews (QBRs) with a focus on value delivery and technology road-mapping.
2. Sales Operations & Technical Coordination
- Act as the bridge between Sales and Technical teams to validate complex orders. You ensure that what is sold is technically viable and properly licensed.
- Assist in the creation of Scopes of Work (SOW), utilizing your organizational skills to ensure no detail is missed during the quoting process.
- Maintain impeccable data hygiene in our CRM/PSA, potentially utilizing AI tools to summarize client interactions and action items.
3. Onboarding & Project Liaison
- Own the critical "Sales-to-Operations" handoff. You are responsible for ensuring the Project Management (PM) team has everything they need to start a project successfully.
- Manage client expectations during the onboarding phase, ensuring clear communication channels are established.
- Troubleshoot roadblocks during the provisioning process so the Sales team can focus on selling and the PM team can focus on deploying.
4. Process Optimization (The "AI Forward" Aspect)
- Be an internal champion for efficiency. Use your independent judgment to suggest new AI tools or automations that could make the Sales or Onboarding process faster.
- Stay educated on the rapidly changing AI landscape (Microsoft Copilot, ChatGPT, Automation platforms) to better converse with clients and improve your own productivity.
What We Are Looking For
Core Attributes:
- Tech-Curious & AI-Forward: You don't just tolerate new technology; you love it. You are likely the person your friends ask about the latest AI tools.
- The "Linchpin": You are excellent at connecting the dots. You know how to communicate technical constraints to salespeople and business urgency to engineers.
- Structured yet Flexible: You are highly organized (a master of the checklist), but you can pivot instantly when a high-priority client issue arises.
- Independent Problem Solver: You don't wait for permission to fix a broken process. You bring solutions to the table.
Qualifications:
- 2+ years of experience in an MSP, SaaS, or Technical Account Management role.
- Passion for AI & Automation: Demonstrated interest in or experience with using AI tools to increase productivity is a major plus.
- Strong understanding of the MSP lifecycle (Ticketing, Quoting, Onboarding).
- Experience with MSP tools (ConnectWise, Autotask, HubSpot, etc.) is preferred.
- Excellent written and verbal communication skills—you can translate "Geek" to "English."
Why Join Iserv?
- Be on the Cutting Edge: We are actively integrating AI into the MSP model. You will learn skills here that will define the next decade of the IT industry.
- Impact: You won't be a cog in a wheel. Your ability to organize and automate will directly impact our company's bottom line and efficiency.
- Health, 401k, PTO
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 3,897 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At iServ, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
iServ AI Hiring
iServ has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Grand Rapids, MI, US, Orlando, FL, US, Dedham, MA, US.
Location Context
Across all AI roles, 16% (615 positions) offer remote work, while 3,251 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 3,897 open positions tracked in our dataset. By seniority: 111 entry-level, 1,958 mid-level, 1,413 senior, and 415 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (615 positions). The remaining 3,251 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 3,897 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,733), Data Scientist (273), AI Software Engineer (271). 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 (111) are outnumbered by mid-level (1,958) and senior (1,413) 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 415 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (615 positions), with 3,251 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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 (2,064 postings), Aws (1,085 postings), Azure (867 postings), Rag (865 postings), Gcp (697 postings), Pytorch (650 postings), Prompt Engineering (597 postings), Kubernetes (499 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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