Interested in this AI/ML Engineer role at Network Doctor?
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About This Role
Network Doctor is an IT managed services firm where company culture and teamwork are essential. Our firm provides IT support to hundreds of SMB companies in the NY\-metro area, across a diverse range of industries including finance, healthcare, real estate, non\-profit, and others.
We are focused on creating and maintaining a comfortable, collaborative, and inclusive environment, where everyone has a voice and all suggestions will be given consideration, as long as they are delivered in a productive and respectful manner. This ideology is at the core of who we are and want to be. We are looking to hire individuals with a passion for working in a team environment alongside many talented technology and administrative professionals.
About the Team:
We are launching a new initiative to offer automation and AI consulting services alongside our traditional managed IT offerings. Because we already maintain trusted relationships with hundreds of organizations, this role has a unique opportunity to bring new capabilities to an established client base.
About the Role:
The AI Solutions Architect / Automation Consultant works directly with clients to identify operational inefficiencies and design practical AI and automation solutions that improve workflows, reduce manual work, and create measurable business value.
This role is approximately 75% client\-facing, with the remaining time focused on developing repeatable automation services that can be delivered consistently by our internal teams.
Success in this role requires a different skill set than traditional MSP engineers. We are looking for someone who combines business consulting ability with strong understanding of modern AI and automation tools, and who can translate operational challenges into practical solutions.
The candidate should be a motivated individual looking to work in a stimulating, fast\-paced and fun atmosphere. Responsibilities will be diverse and often require personal judgment. This position represents the beginning of a new service line within our company, creating significant growth opportunities for the individual who helps build and shape it.
The team member will work at our Englewood Cliffs, NJ office, daily and occasionally travel onsite to our clients when needed.
Key Responsibilities
- Meet with clients to understand business processes, inefficiencies, and workflow challenges
- Identify opportunities to improve operations using AI and automation
- Design practical automation and AI\-enabled solutions \- these should all be INCREMENTAL things that build over time, rather than broad sweeping initiatives. Think implementation within 30\-90 days for any solution.
- Translate business problems into structured project scopes and implementation plans – and get buy in from the clients
- Present recommendations to client leadership in clear, business\-focused language
- Quantify ROI including time savings, efficiency gains, and cost reduction
- Develop repeatable AI and automation services that can scale across our client base
- Create SOWs, documentation, and delivery frameworks for new services
- Evaluate emerging AI tools and automation platforms for practical client use
Requirements:
- Experience in consulting, solution architecture, or client advisory roles
- Strong understanding of business processes and workflow optimization
- Ability to identify automation opportunities within organizations
- Excellent communication skills and ability to explain complex concepts simply
- Comfort presenting solutions to business leadership and decision makers
- Strong analytical thinking and problem\-solving skills
- Organized, adaptable, and comfortable working in a fast\-moving environment
Preferred:
- Experience designing AI or automation\-driven workflow solutions
- Familiarity with modern AI and automation tools such as:
- Microsoft Copilot / Azure AI
- OpenAI / GPT\-based tools
- Power Automate / Microsoft Power Platform
- Zapier / Make (Integromat)
- Replit, Lovable, or similar AI\-assisted development platforms
- Low\-code / no\-code workflow tools
- Experience in creating SOWs, project scopes, and implementation documentation
- MSP experience is helpful but not required
- Proficiency with Microsoft 365 and modern productivity platforms
Pay: $120,000\.00 \- $150,000\.00 per year
Benefits:
- 401(k) matching
- Dental insurance
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Retirement plan
- Vision insurance
Work Location: In person
Salary Context
This $120K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 951 roles with salary data).
View full AI/ML Engineer salary data →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 1,809 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Network Doctor, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($135K) sits 27% below the category median. Disclosed range: $120K to $150K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Network Doctor AI Hiring
Network Doctor has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Englewood Cliffs, NJ, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 16% (294 positions) offer remote work, while 1,505 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 1,809 open positions tracked in our dataset. By seniority: 34 entry-level, 797 mid-level, 728 senior, and 250 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (294 positions). The remaining 1,505 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 1,809 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,274), Data Scientist (145), AI Software Engineer (132). 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 (34) are outnumbered by mid-level (797) and senior (728) 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 250 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (294 positions), with 1,505 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (877 postings), Aws (592 postings), Azure (458 postings), Rag (380 postings), Gcp (364 postings), Pytorch (277 postings), Prompt Engineering (266 postings), Claude (250 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.
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