Interested in this AI/ML Engineer role at Summit Consulting, LLC?
Apply Now →About This Role
Summit provides independent insurance agents and their clients with market\-leading workers’ comp insurance and elite customer service. We’re growing and currently serve more than 7,000 independent agents and 31,000 policyholders across the South, Midwest, and Mid\-Atlantic.
Our strength comes from a single\-minded focus on workers’ comp and a commitment to long\-term customer relationships. Summit is who you turn to when you need a specialist who not only knows workers’ comp but also gets to know your business and your priorities.
Our field team members are located in every state in which we operate.
Summit is a member of the Great American Insurance Group where our highest goal is for all employees to feel included, respected, and empowered to perform at their best.
Essential Job Functions and Responsibilities
- Partner with IT, product, business, and shared service teams to surface, develop, track, and move AI initiatives from intake through adoption, ensuring alignment to enterprise priorities, governance expectations, and delivery objectives.
- Help shape the operating model and ways\-of\-working standards for how AI initiatives move through the enterprise, establishing consistent processes, governance expectations, decision frameworks, and delivery practices.
- Facilitate cross\-functional alignment sessions across IT, business, engineering, architecture, and shared service partners to drive prioritization, resolve dependencies, enable decision\-making, and maintain momentum across AI initiatives.
- Continuously improve the operating model and ways of working to scale the AI program, identifying opportunities to increase consistency, transparency, efficiency, and effectiveness across participating teams and stakeholders.
- Lead end\-to\-end discovery and requirements definition for AI and automation initiatives, ensuring clear problem framing, scope definition, assumptions, success measures, and alignment with enterprise objectives.
- Own the creation and maintenance of high\-quality documentation and implementation artifacts, including business requirements, user journeys, process flows, automation and AI runbooks, governance materials, and decision documentation.
- Prepare decision\-ready materials and provide portfolio\-level visibility into initiative status, risks, dependencies, tradeoffs, and outcomes to support leadership reviews, governance activities, and executive decision\-making.
- Escalate risks, dependency conflicts, and stalled initiatives with actionable recommendations, while providing senior\-level analysis and project support across AI workstreams to drive successful outcomes.
- Great American’s culture is built on connection, shared learning, and strong relationships. To support this, employees in this role are expected to be on\-site four days a week, with the flexibility to work one day remotely. Core in\-office days are Tuesday–Thursday, with the fourth day determined by business needs.
Job Requirements
### Education: Bachelor's degree in Business, Information Technology, Engineering, Computer Science, or a related field.
### Experience: Generally, a minimum of 8 years of experience in program management, product operations, business analysis, transformation, AI, automation, technology delivery, or related roles. Experience supporting AI, data, automation, or technology\-driven initiatives required.
### Scope of Job/Qualifications:
- Demonstrated ability to help shape and support governance frameworks, operating cadences, intake processes, prioritization methodologies, and cross\-functional decision forums.
- Leads discovery, requirements definition, and business analysis activities for complex initiatives, translating business needs into well\-defined, implementation\-ready requirements and artifacts.
- Strong experience creating and maintaining high\-quality documentation, including business requirements, process flows, operating procedures, governance materials, decision records, and executive\-ready communications.
- Demonstrates strong judgment and decision\-making skills, with the ability to operate effectively in ambiguity while balancing competing priorities and stakeholder interests.
- Strong analytical, organizational, and project management skills, including the ability to gather and analyze information, manage risks and dependencies, track progress, and support successful delivery outcomes.
- Excellent written and verbal communication skills with the ability to translate complex initiatives into clear, actionable updates for stakeholders and senior leadership.
- Recognized as a trusted partner who effectively facilitates cross\-functional collaboration, drives alignment, and helps scale enterprise programs through continuous improvement of processes and ways of working.
Company:
SCI Summit Consulting, LLCBenefits:
We offer competitive benefits packages for full\-time and part\-time employees\*. Full\-time employees have access to medical, dental, and vision coverage, wellness plans, parental leave, adoption assistance, and tuition reimbursement. Full\-time and eligible part\-time employees also enjoy Paid Time Off and paid holidays, a 401(k) plan with company match, an employee stock purchase plan, and commuter benefits.
Compensation varies by role, level, and location and is influenced by skills, experience, and business needs. Your recruiter will provide details about benefits and specific compensation ranges during the hiring process. Learn more at http://www.gaig.com/careers.
- Excludes seasonal employees and interns.
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 Summit Consulting, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
Summit Consulting, LLC AI Hiring
Summit Consulting, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lakeland, FL, US.
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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