Interested in this AI/ML Engineer role at Impact Networking, LLC?
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Impact exists because a small group of industry leaders believed companies in our space were doing it wrong. Many prioritized margins while the people doing the work and the clients relying on it became an afterthought. Impact was built differently: invest in great people, trust them to do great work, and deliver the kind of service clients remember. That approach helped us grow from a small office technology company into a national managed services provider, while remaining privately owned and people-first.
What makes Impact special is that growth has never come at the expense of the values that got us here. We continue to reinvest in our people, our capabilities, and the opportunities ahead so employees can grow alongside the business. We’ve built something special, but we’re far from finished. The people who join Impact now will help define what comes next.
Job Overview
The Associate Consultant is a growth-oriented sales and consulting role for someone ready to build expertise in AI and Applications solutions while helping turn qualified, net-new opportunities into profitable revenue. Most access is generated by branch AEs and SDRs, and this Consultant will also learn to create additional access when branch-sourced opportunities are not sufficient to hit target volume. Rather than operating through a clean hand-off, the Consultant partners closely with the AE, with each person owning different aspects of the engagement—especially through proposal, negotiation, and close. With support from Solution Architects/SMEs and branch leadership, this Consultant develops into the technical and commercial expert who helps convert access into signed, profitable business. As a foundational sales role within AI Solutions’ New Business channel, it is well suited to someone with strong consulting instincts, executive presence, and the motivation to grow their expertise while taking meaningful ownership of results.
Responsibilities
- Partner with branch AEs and SDRs to help convert net-new opportunities into revenue, while building the skills to self-generate additional pipeline when branch-sourced access is not sufficient to hit target volume.
- Support and increasingly lead business discovery and opportunity maturation in partnership with the AE—not as a clean hand-off, but as a shared effort where each person owns different aspects of the engagement.
- Build the business case and commercial strategy, including cost justification and ROI/value-based analysis, in coordination with Solution Architects/SMEs for technical validation.
- Build familiarity with—and sell—the complete approved AI and Applications portfolio, attaching Managed AI Operations where appropriate, with support from Solution Architects/SMEs as your solution expertise develops.
- Grow into ownership of the solution and commercial elements of pricing, proposal, negotiation, and close, working jointly with the AE rather than taking sole ownership.
- Maintain disciplined CRM and forecast hygiene across the full opportunity lifecycle.
- Retain active solution/expansion credit for a fixed six months after signature on any deal originated through this role, per AI Solutions’ standard post-signature credit window.
- Contribute to AE/SDR enablement by helping branch sales staff qualify and position AI opportunities correctly as your solution expertise develops.
Qualifications & Experience
- Relevant sales, business development, customer-facing, or sales-consulting experience. Exposure to AI, SaaS, Data & Analytics, Microsoft, Cloud Transformation, or another enterprise technology category is helpful but not required.
- Strong consulting potential, with the curiosity and coachability to learn how to run discovery, build a business case, and move a deal from qualified opportunity to close.
- Comfort working in a true partnership model with branch AEs/SDRs, sharing ownership of proposal, negotiation, and close rather than relying on a clean hand-off, while developing the confidence to self-generate pipeline when branch-sourced access falls short.
- Strong written and verbal communication skills, with the ability to build credibility with executive buyers and grow confidence discussing both business value and technical fit.
- Analytical aptitude and willingness to learn how to build cost-justification, ROI, and value-based business cases that support proposals.
- Organized and detail-oriented, with the ability to develop disciplined CRM and forecast habits.
- Willingness to work from an assigned Impact office and travel locally within that territory as needed.
What Drives Impact
Behaviors define what our core values look like in action. Leaders are expected to model them; teams are expected to live them:
- Innovation: We embrace change because innovation lives outside the comfort zone.
- Passion: We are driven by purpose, fueled by passion, and obsessed with making an impact.
- Honesty: We are fiercely transparent and consistently honest.
- Fun: We fuel work with fun, knowing life's too short for boring.
- Low Ego: We champion ideas over titles, because brilliance knows no rank.
- One Team: We win as a team, we lose as a team, we are one team.
Compensation
Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skillset, experience and training, licensures and/or certifications, and other organizational needs.
Impact may offer applicable incentive compensation plans depending on role and/or department.
Full compensation details can and will be discussed with an Impact Talent Acquisition team member at the start of the interview process.
Benefits We Take Pride In
- 5-year Tiffany & Co. Gift Card or Custom Menswear, 10-year Custom Rolex, 20-year $20,000 Check incentive rewards
- Valuable time off with up to 20 days of PTO, 7 Paid Sick Days, 12+ paid holidays, and Paid Parental Leave
- Development and growth opportunities with on-going training & continued education reimbursement
- 401(K) & retirement plans with complimentary financial advisory services
- Comprehensive health, disability, life, dental, and vision plans
For full benefit information, visit https://www.impactmybiz.com/careers/#benefits
Work Authorization & Immigration Sponsorship
Candidates must be authorized to work in the United States at the time of application. Immigration sponsorship may be considered in select cases based on business need, cost, workforce planning, and applicable government requirements. Impact does not guarantee sponsorship for any visa category and may decline certain petitions based on associated costs or regulatory requirements.
Certain positions within Impact may involve access to information, technology, software, or technical data that is subject to U.S. export control laws and regulations, including the International Traffic in Arms Regulations (ITAR) and the Export Administration Regulations (EAR). Where required by applicable law based on the nature of the role, individuals in such positions must qualify as a “U.S. Person,” as defined by law, or otherwise be eligible to obtain any required government authorizations.
Compensation Range: $60K - $70K
Salary Context
This $60K-$70K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Impact Networking, LLC, 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 $211,950 based on 13,048 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000. This role's midpoint ($65K) sits 69% below the category median. Disclosed range: $60K to $70K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Impact Networking, LLC AI Hiring
Impact Networking, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $70K - $70K.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% below the national 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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