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About This Role
ITX is looking for a Tech Lead (Full Stack \& Agentic AI) in the United States of America.
About the Role
We're looking for a Tech Lead to help drive the next generation of AI\-powered solutions at ITX.
In this role, you'll lead the development of modern applications built with React and Rails while helping our teams expand into Agentic AI Development. You'll partner closely with engineers, product teams, architects, and client stakeholders to deliver scalable solutions that solve real business problems.
We're looking for someone who enjoys staying hands\-on with technology while leading technical direction, mentoring engineers, and building strong relationships with clients and stakeholders.
*Note:* *This opening is only available for candidates based in the United States of America. Applications from other locations will not be considered for the role.*
What You'll Do
- Lead the design and delivery of full\-stack applications built with React and Rails
- Guide architectural decisions and establish engineering best practices
- Mentor engineers and foster a collaborative team culture
- Partner with Product, Architects, and client stakeholders to align technical solutions with business goals
- Drive code quality through reviews, testing, and continuous improvement
- Help design and implement Agentic AI solutions and intelligent workflows
- Identify risks, dependencies, and opportunities across systems and teams
What We're Looking For
- Strong hands\-on experience with React and Rails
- Experience leading technical teams and delivering complex software solutions
- Solid understanding of modern application architecture, APIs, databases, and cloud\-native systems
- Experience with AWS, Terraform, GitHub Actions, and modern CI/CD practices
- Understanding of Agentic AI Development and modern AI application patterns
- Strong problem\-solving skills and systems\-thinking mindset
- Outstanding communication and stakeholder management skills
- Ability to influence technical decisions and build alignment across teams
- Agile/Scrum experience
Nice to Have
- Experience with Amazon Bedrock, AgentCore, or similar AI platforms
- Experience in consulting or client\-facing environments
- Passion for mentoring and team development
ITX’s Compensation Philosophy
Equality in compensation has been our practice since ITX started in 1997\.
ITX believes that market\-based pay ensures fair and equitable compensation for our worldwide team members and pay that is based on the market, not on who has the best negotiation skills. At ITX, you’ll never discover that someone in the same job with the same experience makes more than you, or that there are pay gaps based on race, gender, disability, or age.
How do our team members experience market\-based pay at ITX? We gather market data to benchmark each position in our candidates’ and team members’ locations and use these benchmarks for candidate offers and to perform regular compensation reviews for our team members. You’ll never have to worry about asking for a pay raise again. At least once a year ITX automatically adjusts pay when the benchmark is higher than our team member’s compensation.
In Rochester, N.Y., home to ITX’s headquarters, the pay range for a Tech Lead role is $105,000 to $150,000, depending on experience, specific skills and certifications, and education. Based on your location in the United States if you are in a place where the market for your role is higher or lower, this pay range could be 8% lower or 9% higher than the Rochester, N.Y. market.
Do you have questions about ITX’s compensation practices? Let us know! We’re proud of how we do compensation at ITX and welcome the opportunity to share more.
*This role was posted by ITX on August 3, 2026\.*
Salary Context
This $105K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At ITX Corp, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($127K) sits 41% below the category median. Disclosed range: $105K to $150K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
ITX Corp AI Hiring
ITX Corp has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rochester, NY, US. Compensation range: $150K - $150K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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