Interested in this AI/ML Engineer role at Novaspect?
Apply Now →Skills & Technologies
About This Role
Novaspect, Inc., an Emerson Local Business Partner and a global leader in process systems and solutions, is currently offering an opportunity for an AI Transformation Lead to join our Technology team in Schaumburg, IL. Reporting to the Vice President of Technology, this strategic and hands\-on leader will guide the responsible adoption and enterprise\-wide application of artificial intelligence—partnering across the organization to identify high\-value opportunities, improve business processes, and move practical AI\-enabled solutions from concept through implementation, adoption, and measurable business outcomes.
The ideal candidate will combine business judgment and technical fluency with a demonstrated ability to build trust, influence stakeholders, and lead enterprise change. This individual will translate emerging AI capabilities into scalable business solutions, establish effective governance and guardrails, and help move Novaspect from experimentation to repeatable, responsible AI adoption and measurable business value.
AI TransformationLead Essential Duties and Responsibilities
*AI Strategy and Business Alignment*
- Serve as a strategic advisor to the Vice President of Technology and business leaders on where AI—including generative and agentic AI—creates the greatest value and where it introduces risk
- Develop and maintain a prioritized AI roadmap aligned with Novaspect’s strategic objectives, operating plans, resource capacity, and technology investments
- Identify, evaluate, and sequence high\-impact use cases based on their potential to improve efficiency, quality, customer experience, employee productivity, decision\-making, and other business outcomes, while considering feasibility, organizational readiness, scalability, and risk
- Monitor the evolving AI landscape and recommend the technologies, skills, resources, partnerships, and capabilities required as Novaspect’s AI transformation program matures
*AI Solution and Process Delivery*
- Lead and coordinate the design, deployment, and ongoing optimization of AI and automation solutions—including generative AI, agentic AI, intelligent workflows, and enterprise knowledge solutions—that address business challenges and improve core processes
- Establish a scalable approach for moving AI use cases from concept through design, pilot, production, monitoring, and continuous improvement, with appropriate evaluation, human oversight, and guardrails
- Evaluate whether business needs are best addressed through process redesign, existing technology, automation, generative AI, agentic AI, or a combination of approaches
- Assess process, data, knowledge, integration, and organizational readiness, partnering with internal technical resources and external providers to ensure solutions are secure, scalable, supportable, and aligned with enterprise architecture and business requirements
*Governance, Risk, and Responsible AI*
- Develop and maintain Novaspect’s AI governance policies, standards, and responsible\-use guidance, partnering with leadership to keep them current as technologies, risks, regulations, and business needs evolve
- Design practical, tiered, risk\-based governance that enables innovation while defining appropriate human review, approval, accountability, and escalation requirements
- Establish governance across the full AI lifecycle, including evaluation, approval, deployment, monitoring, modification, revalidation, incident response, and retirement
- Partner closely with IT Security, Legal, Compliance, Human Resources, and Data Governance to integrate AI\-specific risks into existing controls and support responsible AI practices, including fairness, reliability and safety, privacy and security, transparency, accountability, and human oversight
*Enablement, Adoption, and Change Management*
- Develop and deliver role\-based training, guidance, and resources that help employees and managers safely and effectively use AI tools, including Microsoft 365 Copilot and other approved enterprise platforms
- Lead change management activities that address the people, process, and technology impacts of AI\-enabled transformation and support sustained adoption
- Serve as a visible and approachable guide, coach, and subject\-matter resource while building and supporting a network of AI champions, super users, and functional advocates across the organization
- Gather employee and stakeholder feedback and use it to continuously improve solutions, training, communications, and adoption strategies
*Value Realization and Continuous Improvement*
- Establish clear business outcomes, baseline measures, benefit assumptions, and success criteria for AI initiatives before implementation
- Manage the AI initiative portfolio in partnership with business sponsors, ensuring initiatives remain aligned with business scope, strategic priorities, and available resources. Track progress, adoption, costs, capacity created, financial and operational value, and realized business outcomes
- Conduct post\-implementation reviews and recommend whether solutions should be enhanced, expanded, redesigned, or retired
- Develop reusable standards, solution patterns, playbooks, and evaluation methods that allow successful approaches to scale across Novaspect
*Stakeholder and Partner Leadership*
- Serve as the primary coordinating leader and visible representative for Novaspect’s enterprise AI transformation efforts, building trusted and transparent relationships across the organization
- Communicate priorities, progress, risks, decisions, changes, and operational impacts clearly and consistently, translating complex technical concepts into practical business language for nontechnical stakeholders and executive leadership
- Establish clear ownership, decision rights, resource commitments, and accountability across business sponsors, Technology, IT Security, Legal, Human Resources, Data Governance, and other participating functions
- Evaluate and oversee technology providers, consultants, and implementation partners supporting AI initiatives, including vendor performance, knowledge transfer, documentation, security compliance, and long\-term supportability
AI TransformationLead Required Qualifications
- Eight or more years of progressive experience in technology, business consulting, digital transformation, automation, enterprise applications, or a related field, including leadership of complex cross\-functional initiatives
- Practical experience evaluating, prototyping, deploying, or overseeing generative and agentic AI solutions, with working knowledge of large language models, grounding and retrieval techniques, solution evaluation, human oversight, and guardrails
- Demonstrated success translating complex business challenges into prioritized use cases, business cases, technology\-enabled solutions, implementation plans, and measurable business outcomes
- Experience leading initiatives from discovery and process design through implementation, adoption, performance measurement, and continuous improvement
- Experience applying governance and responsible AI practices to technology initiatives, including risk management, privacy, security, accountability, transparency, and human oversight
- Demonstrated experience leading change management, communication, training, stakeholder engagement, and adoption activities for new technologies and ways of working
- Proven ability to influence and collaborate with executive leaders, business stakeholders, technical teams, cross\-functional governance partners, and external providers
- Strong communication, facilitation, analytical, prioritization, and problem\-solving skills.
AI TransformationLead Preferred Qualifications
- Experience in both consulting and an internal business, technology, or operational role
- Experience in industrial automation, manufacturing, engineering, field service, distribution, professional services, or a related business\-to\-business environment
- Experience with enterprise AI and automation platforms, including Microsoft 365 Copilot, Copilot Studio, Power Platform, Azure AI services, or similar technologies
- Experience designing or deploying AI assistants, intelligent workflows, enterprise knowledge solutions, automations, or AI agents across multiple functions or in an enterprise environment
- Experience establishing an enterprise AI governance framework, center of excellence, transformation program, or structured AI operating model
- Advanced degree in artificial intelligence, business, technology, engineering, or a related discipline, or relevant certification in AI, change management, program management, or a related field
AI TransformationLead Key Attributes
- Curious, technically fluent, and learning\-agile, with the ability to remain current in a rapidly evolving field
- Approachable and relationship\-oriented, with the ability to build trust and serve as a visible guide, coach, and mentor
- Strategic and pragmatic, balancing innovation and speed with business value, responsible use, operational feasibility, and risk
- Execution\-oriented and composed under pressure, with sound judgment, integrity, attention to detail, and the ability to create structure in ambiguous environments
AI TransformationLead Physical Requirements:
- Ability to work extended hours on a computer
AI TransformationLead Pay:
- Base Salary: $130,000 \- $160,000
- Bonus Potential: 5%
*T**he* *final offer will be based on several factors, including, but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in the position.*
AI TransformationLead Benefits: Recognized with a Top Employee Benefit Plan Award in 2020, below you will find our outstanding total rewards package when you join our team including:
- Generous paid time off; starting at 15 vacation days, 10 holidays, and 10 days of Paid Sick \& Safety Time (PSST)
- 401K with 6% company match
- Employee Stock Ownership Plan (ESOP)
- Excellent health \& wellness benefits
- Student debt \& tuition reimbursement
Who We Are:
Novaspect, Inc., is an employee\-owned company that engineers, sells, and services industrial process controls. Our Core Purpose is to improve our customer’s performance through the innovative application of technology. We are passionate about creating effective processes and building customer relationships. We position ourselves to attract the best talent, and ensure we are delivering local services with proven technologies.
Salary Context
This $130K-$160K 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 Novaspect, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($145K) sits 34% below the category median. Disclosed range: $130K to $160K.
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.
Novaspect AI Hiring
Novaspect has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Schaumburg, IL, US. Compensation range: $160K - $160K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.