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About This Role
Technology Leader \| Enterprise IT \| AI\-Enabled Business Transformation \| Manufacturing Innovation
We are seeking a hands\-on, business\-minded technology leader to lead enterprise IT operations and accelerate practical AI innovation across a multi\-location manufacturing environment. This is a high\-impact leadership opportunity for an IT leader who can operate strategically while staying close enough to the technology to drive execution, solve complex problems, and deliver measurable business value.
The Director of IT \& AI Innovation will serve as the company’s technology catalyst by owning the reliability, security, and scalability of core systems while identifying and deploying AI\-enabled solutions that improve business workflows, execution, visibility, effectiveness, and operational decision\-making.
The Opportunity
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- Lead enterprise IT: Own global IT infrastructure, cybersecurity, systems administration, and technology support across manufacturing and corporate location.
- Turn AI into business advantage: Move beyond AI theory by building, selecting, and implementing practical tools that automate processes, improve reporting, and increase team productivity.
- Shape the digital backbone of manufacturing: Serve as the functional and technical leader for our Odoo ERP and related business systems spanning engineering, production, inventory, finance, and commercial teams.
- Partner across the business: Translate complex technical possibilities into clear business outcomes.
What You Will Lead
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- IT Operations \& Infrastructure: Lead day\-to\-day and long\-range IT operations, including systems administration, network security, uptime, user support, hardware/software lifecycle management, and multi\-site technology readiness.
- AI Innovation \& Automation: Identify, pilot, and deploy AI\-enabled tools that reduce manual work, improve knowledge flow, accelerate engineering and sales cycles, and strengthen decision\-making.
- ERP \& Business Systems: Own the performance, adoption, optimization, and integration of Odoo MRP/ERP and related platforms across engineering, production, inventory, finance, and reporting functions.
- Business Reporting \& Analytics: Build reliable, scalable reporting capabilities that give leaders clear visibility into performance, trends, risks, and opportunities.
- Team Leadership: Lead, mentor, and scale a lean, high\-performing team of IT system administrators and ERP coordinators while maintaining strong service delivery standards.
- Strategic Projects: Plan and execute critical technology initiatives, including facility expansions, cloud migrations, security improvements, system upgrades, and vendor transitions.
- Vendor \& Budget Management: Negotiate with ISPs, software providers, hardware partners, and technology vendors to maximize service quality, cost discipline, and return on investment.
What Success Looks Like
- Enterprise systems are stable, secure, scalable, and responsive to business needs.
- AI and automation initiatives move from concept to measurable adoption, improving speed, accuracy, and productivity across departments.
- Odoo and related business systems become stronger engines for operational visibility, workflow discipline, and cross\-functional alignment.
- Leaders have access to clear, trusted reporting that supports faster and better decisions.
- The IT team is respected as a proactive business partner, not just a support function.
The Ideal Candidate
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You are a technically credible IT leader who earns trust through execution. You are equally comfortable discussing digital strategy with senior leadership, solving infrastructure issues with your team, evaluating ERP workflows with operations, and experimenting with AI tools that create immediate business value.
- Strategic thinker with a strong bias for action and practical implementation.
- Hands\-on technical leader with broad infrastructure, security, cloud, ERP, and support experience.
- Clear communicator who can translate technology into business outcomes.
- Curious innovator who understands how to apply AI responsibly and effectively in real business environments.
- Collaborative leader who builds credibility across executive, operational, and technical audiences.
- Resilient problem\-solver who thrives in fast\-moving manufacturing or engineered\-products environments.
Preferred Background
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The strongest candidates will bring a blend of executive presence, technical depth, manufacturing fluency, and demonstrated success turning technology into competitive advantage.
Qualifications
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- Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field preferred; equivalent hands\-on experience will be considered.
- 5\+ years of progressive IT experience, including leadership responsibility in a manufacturing, engineered\-products, or multi\-site operating environment.
- Hands\-on expertise with enterprise infrastructure, Windows/Linux server administration, cloud environments such as AWS or Azure, network security, and hardware/software troubleshooting.
- Direct experience managing and optimizing Odoo or a comparable mid\-market ERP system such as NetSuite, Epicor, or Infor.
- Demonstrated ability to evaluate, implement, and scale AI or automation tools that improve business workflows and decision\-making.
- Strong project management capability across system upgrades, cloud migrations, facility expansions, vendor transitions, and cross\-functional technology initiatives.
Why This Role Stands Out
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This is not a maintenance\-only IT role. It is an opportunity to define how a manufacturing business uses technology, data, ERP discipline, and AI to work smarter, move faster, and compete more effectively. For a technology leader who wants ownership, visibility, and the ability to make a tangible impact, this role offers a rare combination of strategy, execution, and innovation.
Ready to lead the next chapter of IT and AI innovation? We invite accomplished technology leaders to bring their vision, technical depth, and execution mindset to a role where their work will be visible, valued, and business\-critical.
Salary ranges for this position vary by job location and are determined based on experience, reflecting our commitment to recognizing individual expertise and contributions.
Successful Candidate must be able to meet U.S. export control requirements (ITAR/EAR) to gain access to technical data. This position requires access to technology that is subject to U.S. export control regulations. Candidates must be eligible for employment in the US and meet the requirements of ITAR.
We are an Equal Opportunity Employer and do not discriminate based on any legally protected status. Qualified applicants will receive consideration based on merit and business needs, and reasonable accommodations are available for individuals with disabilities. This job description is not intended to be all‑inclusive, and duties or requirements may change as business needs evolve. Employment is at will and may be terminated by either the employee or the company at any time, with or without cause or notice, in accordance with applicable law.
Salary Context
This $140K-$190K range is below the median 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 Conax Technologies, 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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($165K) sits 25% below the category median. Disclosed range: $140K to $190K.
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.
Conax Technologies AI Hiring
Conax Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Buffalo, NY, US. Compensation range: $190K - $190K.
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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