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Job Purpose:
The Director of IT \& AI Innovation at CONAX TECHNOLOGIES LLC will lead the strategic direction and execution of technology initiatives, focusing on leveraging artificial intelligence to drive innovation and improve business processes. The role involves overseeing the development and implementation of cutting\-edge IT solutions that align with the company's goals, enhancing operational efficiency, and maintaining a competitive edge in the industry.
Key Responsibilities:
- Develop and execute a comprehensive IT and AI strategy that supports the company's business objectives and fosters innovation.
- Lead and manage a team of IT professionals and AI specialists, providing guidance, mentorship, and fostering a culture of continuous improvement.
- Oversee the design, development, and deployment of AI\-driven solutions to enhance business processes and decision\-making capabilities.
- Collaborate with cross\-functional teams to identify opportunities for technology\-driven improvements and innovation.
- Ensure the security, scalability, and reliability of IT systems and infrastructure, implementing best practices in cybersecurity and data protection.
- Stay abreast of emerging technologies and industry trends, evaluating their potential impact on the company and integrating relevant innovations into the business.
- Manage relationships with external vendors and partners to ensure the delivery of high\-quality IT services and solutions.
- Monitor and report on the performance of IT and AI initiatives, using data\-driven insights to inform strategic decisions and optimize outcomes.
- Drive the adoption of agile methodologies and innovative practices within the IT department to enhance project delivery and responsiveness to business needs.
- Lead efforts to promote a culture of innovation across the organization, encouraging experimentation and the exploration of new ideas and technologies.
Required Education:
- Bachelor’s degree in Computer Science, Information Technology, Artificial Intelligence, or a related field is required.
- A Master’s degree in Business Administration (MBA) or a related field is highly preferred.
Required Experience:
- Minimum of 10 years of experience in IT management, with a focus on strategic planning and execution.
- Proven track record of at least 5 years in leading AI innovation projects and initiatives.
- Experience in managing cross\-functional teams and collaborating with stakeholders at various levels of the organization.
- Demonstrated experience in budget management and resource allocation for large\-scale IT projects.
- Previous experience in a leadership role within a technology\-driven organization is essential.
Required Skills and Abilities:
- Strong understanding of AI technologies, machine learning models, and data analytics.
- Excellent leadership and team management skills, with the ability to inspire and motivate staff to achieve strategic goals.
- Superior problem\-solving skills and the ability to think critically and strategically.
- Exceptional communication skills, both written and verbal, with the ability to articulate complex technical concepts to non\-technical stakeholders.
- Proficiency in project management methodologies and tools, with a track record of delivering projects on time and within budget.
- Ability to foster innovation and drive continuous improvement in IT and AI processes and systems.
- Strong interpersonal skills and the ability to build and maintain relationships with internal and external partners.
- High level of adaptability and resilience in a fast\-paced and constantly evolving technological environment.
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: $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 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($165K) sits 23% below the category median. Disclosed range: $140K to $190K.
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.
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, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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