Interested in this AI/ML Engineer role at Resultant?
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Company Description
Resultant is a modern consulting firm with a radically different approach to solving problems.
We don’t solve problems for our clients. We solve problems with them.
Through outcomes driven by data analytics, technology solutions, digital transformation, and beyond, our team works with clients in both the public and private sectors to solve their most complex challenges. We start by learning as much as we can about who they are, how they work, and what they’re striving for so we can feel their problems as our own. Partnering with our clients means their desired outcomes are always top of mind, their challenges and strengths guiding our efforts. We build client\-focused relationships before we build unique solutions that blaze past expectations.
Originally founded in Indianapolis in 2008, Resultant now employs more than 400 team members who operate from offices around the United States including Indianapolis and Fort Wayne, Indiana; Columbus, Ohio; Lansing, Michigan; Denver, Colorado; Dallas, Texas and Atlanta, Georgia.
We’re Resultant. Clients partner with us to see a difference. People join us to make one.
Job Description
As a Manager, you will own end\-to\-end delivery of technology and digital transformation initiatives while developing the consultants on your team. You'll act as a player\-coach—driving client outcomes while building the next generation of Liberty talent—and serve as a trusted advisor to client stakeholders.
Client Delivery \& Project Leadership
- Engagement delivery — Lead technology and digital transformation engagements from scoping through implementation, ensuring on\-time, on\-budget, and high\-quality outcomes.
- Roadmap development — Translate client business objectives into actionable roadmaps spanning systems modernization, cloud migration, data and platform strategy, enterprise applications, and process automation.
- Project economics — Manage scope, staffing, budget, risk, and timeline across concurrent workstreams.
- Client relationships — Serve as a primary point of contact, build trusted relationships with senior stakeholders, and resolve escalations with confidence.
- Problem solving — Structure ambiguous problems and guide teams toward pragmatic, measurable solutions.
People Management \& Team Building
- Coaching — Lead, mentor, and develop consultants and analysts; set clear direction and provide ongoing, constructive feedback.
- Team building — Build and sustain high\-performing delivery teams, balancing utilization with growth and engagement.
- Talent development — Contribute to recruiting, performance reviews, and career development for junior staff.
- Culture — Foster a collaborative, accountable culture aligned with Liberty Advisor Group's values.
Practice \& Growth Contribution
- Identify opportunities to expand engagements and support business development through proposals, scoping, and relationship building.
- Contribute to internal initiatives, delivery methodologies, and the firm's intellectual capital.
Qualifications
- Experience — 6–9 years of relevant experience, including consulting and/or technology and digital transformation delivery.
- Leadership — 2\+ years leading project teams or workstreams, with demonstrated ownership of delivery and client relationships.
- Education — Bachelor's degree required; MBA or relevant advanced degree a plus.
- Domain — Strong background in one or more: digital transformation, technology strategy, cloud/infrastructure, data \& analytics, enterprise applications (ERP/CRM), agile delivery, or process optimization.
- Skills — Excellent communication, executive presence, stakeholder management, and structured problem\-solving abilities.
- Location — Able to work in a hybrid model based in Chicago, with travel as client needs require. Consulting experience strongly preferred.
Additional Information What It's Like to Work Here
At Resultant, we are driven by purpose—partnering with clients to take on their toughest challenges and creating outcomes that make a real difference. Success here means lasting impact, not just delivered projects. We work as collaborative experts, leaning into complexity with confidence and humility, asking smart questions, sharing ideas freely, and combining our diverse expertise to turn challenges into clear, transformative solutions. We make our outcomes visible by sharing results, stories, and lessons learned, because growth happens through shared experience.
Accountability \& Ownership
Resultant offers a flexible, high\-trust environment, paired with a shared commitment to accountability:
- Take ownership of your work from start to finish and deliver on your commitments to clients and coworkers
- Communicate proactively, especially when priorities shift
- Focus on outcomes, follow\-through, and measurable impact
- Show up where it matters, in person or virtually, when it strengthens relationships or results
- Do the small things brilliantly: respond timely, stay organized, and follow through to build trust
We trust each other to own our work and deliver exceptional results. Flexibility means autonomy paired with accountability: the freedom to work in ways that fit your life, balanced with the reliability our clients and coworkers count on.
Is Resultant the right spot for you?
You may thrive here if you bring curiosity and a consulting mindset to every challenge, take ownership of your growth, and give and receive feedback with intention. If you're energized by bold ideas, continuous learning, and investing in the people around you, this is your place. At Resultant, we show up for each other, own our outcomes, and build leaders who create lasting impact for our clients, our communities, and each other.
Equal Opportunity Employer
*We embrace AI across everything we do, and that includes how you prepare for this interview. Feel free to use AI tools to research the role, practice your responses, and put your best foot forward. What matters to us is getting to know the real you — how you think, how you communicate, and what you genuinely bring to the table. Our interviews are designed to go deeper than any polished answer, so come curious and come as yourself without the support of an AI tool during our conversation.*
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 Resultant, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Resultant AI Hiring
Resultant has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% 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 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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