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About This Role
Summary
We’re looking for a builder first: someone who can architect and ship secure dashboards, workflow automations, and AI agents that work in production. You should also be comfortable enough in front of clients to lead discovery, demonstrate solutions, and help close the deal. The build is the job. The commercial ability is what makes you dangerous.
You’ll help establish ITG’s AI practice inside a business with real backing, meaningful upside, and the opportunity to shape the technology, processes, and team from the ground floor.
Responsibilities
- Build secure client\-facing dashboards, internal tools, and workflow applications using platforms such as Base44, Retool, or similar.
- Design AI agent architecture—including model selection, tool calling, memory, retrieval, guardrails, and orchestration—around each client’s workflow.
- Configure and deploy AI agents from initial prototype through testing, production launch, documentation, and support handoff.
- Integrate AI solutions with client systems through APIs, webhooks, databases, and automation platforms.
- Model token usage, API fees, infrastructure costs, and ongoing support requirements so every solution maintains healthy margins.
- Lead or support client discovery sessions to identify process bottlenecks, requirements, risks, and measurable outcomes.
- Translate discovery findings into solution architecture, demonstrations, project scopes, and proposals.
- Support opportunities through the sales process by clearly explaining the proposed solution, value, limitations, and implementation approach.
- Establish reusable development standards, security practices, testing procedures, and deployment processes for ITG’s AI practice.
- Track emerging models, agent frameworks, and development tools, bringing worthwhile technologies into ITG’s toolkit before competitors do.
Requirements
- 3\+ years building software, automation, or technical solutions, ideally within a consulting or client\-facing environment.
- Demonstrated experience taking technical products or solutions from concept through production deployment.
- Hands\-on experience with LLM APIs such as OpenAI, Anthropic, Google Gemini, or comparable platforms.
- Strong understanding of prompt design, structured outputs, function calling, RAG, embeddings, vector databases, agent workflows, and model evaluation.
- Experience building with low\-code or AI application platforms such as Base44, Retool, Bubble, Power Apps, or similar.
- Ability to integrate applications with REST APIs, webhooks, relational databases, and third\-party business systems.
- Working knowledge of JavaScript, Python, or another language used to extend low\-code platforms and build custom integrations.
- Understanding of authentication, role\-based access, data isolation, encryption, secrets management, and secure API practices.
- Familiarity with cloud deployment, development environments, version control, logging, monitoring, and production troubleshooting.
- Ability to design testing and human\-review processes that reduce hallucinations, incorrect actions, and unreliable agent behavior.
- Understanding of AI economics, including token usage, model pricing, infrastructure costs, licensing, and ongoing support requirements.
- Ability to document architecture, workflows, configuration, operating procedures, and client handoff materials.
- Strong project ownership, including the ability to manage priorities, communicate risks, and move a build forward without constant direction.
- Insatiable curiosity: you test emerging AI tools and models independently and can explain plainly why one approach is better than another.
- Comfortable leading client discovery, presenting your work, and contributing to a sales conversation, even if closing is not your primary strength.
Preferred Qualifications
- Experience building solutions for operationally complex industries such as construction, architecture, engineering, manufacturing, professional services, or field service.
- Experience connecting AI solutions with CRM, ERP, document management, ticketing, accounting, or Microsoft 365 environments.
- Experience with AI evaluation frameworks, observability tools, automated testing, or model\-routing strategies.
- Previous consulting, presales, technical sales, or solutions\-engineering experience.
- Experience estimating projects and converting unclear business problems into clearly scoped technical solutions.
Why ITG
You’ll join early enough to define the role rather than fill a seat someone else shaped. ITG offers a community\-benefit mission built into how the company is structured, commission on opportunities you help close, and the chance to build our AI practice from the ground floor.
*Integrated Technology Group is an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status.*
Pay: $90,000\.00 \- $110,000\.00 per year
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Paid time off
- Professional development assistance
- Retirement plan
- Vision insurance
Work Location: In person
Salary Context
This $90K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Integrated Technologies Group, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($100K) sits 54% below the category median. Disclosed range: $90K to $110K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Integrated Technologies Group AI Hiring
Integrated Technologies Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in York, PA, US. Compensation range: $110K - $110K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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