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About Us: EvolutionIQ's mission is to deliver state of the art technology that helps insurance claims teams make claims handling more accurate, fair, and efficient, so that more people impacted by injury or illness can continue their lives with dignity and stability. We are currently experiencing massive growth and to accomplish our goals, we are hiring world\-class talent who want to help build and scale internally, and transform the insurance space. Our team is our \#1 priority, and we have been named one of Inc.'s Best Workplaces 3 years in a row and Built In's Best Places to work in 2025 and 2026!
The Opportunity: We are seeking an lead level Agentic Solutions Consultant to help customers evaluate, design, and adopt EvolutionIQ's next generation agentic solutions. This role will sit at the intersection of Sales, Product, and Engineering. As EvolutionIQ expands into more complex agentic workflows across Disability and P\&C, customers will need more than a standard product sale. They will need a partner who can understand their current operations, systems, workflows, data readiness, governance requirements, and integration constraints; translate that complexity into a clear implementation approach; and build the business case for change.
You will work closely with Product, Sales, Engineering and leadership to support the pre\-sale design process for highly customized enterprise opportunities. This includes helping customers understand how EvolutionIQ's solutions will integrate with their claims systems, policies, eligibility feeds, data flows, and workflows, and shaping the transformation narrative that allows executive sponsors to secure buy\-in internally. You will also help customers distinguish between configurable product capabilities, integration needs, and areas that may require deeper product or implementation planning.
This is a highly consultative, commercially oriented role for someone who can move fluidly between technical details, business case development, solution architecture, and executive storytelling. The ideal person can serve as a trusted advisor to both business and technical stakeholders, helping customers understand the operational, technical, financial, and change management implications of adopting agentic solutions.
About You:
- 5\-10\+ years of experience in solutions consulting, implementation consulting, solutions engineering, solution architecture, or a similar client\-facing role in enterprise software, preferably in insurance
- Experience working on complex systems integration or transformation projects for large enterprise customers
- Ability to understand business processes, map system dependencies, and translate operational complexity into actionable implementation plans
- Experience designing solution architectures for complex enterprise customers, including integrations, data flows, workflow dependencies, implementation risks, and operational controls
- Ability to distinguish between configurable product capabilities, integration needs, product gaps, and true custom requirements
- Strong commercial instincts and comfort supporting pre\-sale conversations alongside sales and product leaders
- Strong written and verbal communication skills, including the ability to create polished customer\-facing decks, plans, business cases, workflow maps, and executive\-ready materials
- Experience operating across both technical and business stakeholders
- Comfort discussing AI adoption topics such as governance, risk, compliance, auditability, data readiness, and responsible operational controls
- Comfortable in ambiguity and excited to help define a new function in a fast\-moving environment
- Experience from firms such as Guidewire, Duck Creek, Capgemini, UiPath, ServiceNow, Palantir, or similar systems integration / insurance technology / enterprise AI environments is a strong plus
In This Role You Will:
- Partner with Sales, Product, and leadership to support pre\-sale solution design for agentic products
- Assess customer operations, workflows, systems, and integration requirements to shape tailored implementation approaches
- Own current\-state and target\-state solution mapping for complex customer opportunities, including systems, data flows, workflow changes, integration points, implementation risks, and success criteria
- Assess customer data readiness, source systems, eligibility feeds, workflow triggers, reporting needs, and operational dependencies required to support successful agentic deployments
- Build customer\-specific business cases, implementation plans, and executive\-ready materials that help customers evaluate and buy complex solutions
- Create detailed documentation and presentation materials that articulate integration points, workflow changes, expected business impact, and implementation considerations
- Create reusable solution design artifacts, workflow maps, reference architectures, ROI templates, customer assessment frameworks, and implementation playbooks that help EvolutionIQ scale the agentic sales motion
- Translate customer discovery into clear internal recommendations for Product, Implementation, Engineering, and Go\-to\-Market teams
- Partner with Product and Engineering to identify recurring customer needs, product gaps, integration blockers, and scalable patterns for future agentic deployments
- Help define how EIQ positions and sells highly customized agentic solutions across different customers and lines of business
- Collaborate with internal stakeholders to develop repeatable frameworks for customer assessments, ROI development, implementation planning, and executive alignment
- Support both technical and commercial conversations with customers, from operational discovery through executive alignment
- Help customers evaluate agentic solutions through the lens of governance, risk, compliance, auditability, operational controls, and responsible AI adoption
- Ensure strong handoff from pre\-sale discovery to implementation by documenting assumptions, technical requirements, customer goals, risks, integration needs, and success criteria
- Help customers think through operating model changes, user adoption, training needs, governance processes, and success metrics required to move from pilot to scaled deployment
Your Impact:
- Customers will have a clearer path to evaluating and adopting EvolutionIQ's agentic solutions
- Sales cycles for complex opportunities will be strengthened by deeper operational discovery, better implementation planning, and stronger executive alignment
- EvolutionIQ will be better equipped to translate product vision into customer\-specific transformation plans
- Product and Sales teams will have a dedicated partner who can bridge technical architecture, business operations, and customer buying processes
- Implementation teams will receive clearer documentation, assumptions, integration requirements, and success criteria before customer work moves from pre\-sale into delivery
- Product and Engineering will gain a stronger feedback loop on customer needs, product gaps, integration blockers, and repeatable solution patterns
- The company will develop a more repeatable motion for selling complex, integration\-heavy agentic products
Work\-life, Culture \& Perks:
- Compensation: The base salary range is $225,000, with flexibility depending on a candidate's background and experience. An annual bonus plan and company equity plan (RSUs) are also included in our compensation package.
- Well\-Being: Medical, dental, vision, short \& long\-term disability, life insurance and AD\&D, and 401k matching. Additional family, wellness, and pet benefits.
- Home \& Family: Paid time off and sick leave, 100% paid parental leave (16 weeks for primary caregivers and 12 weeks for secondary caregivers). We offer a flexible schedule for new parents returning to work.
- Office Life: Catered lunches, happy hours, pet\-friendly spaces, and monthly technology stipend.
- Growth \& Training: $1,000/year for each employee for professional development, as well opportunities for tuition reimbursement.
- Sponsorship: We are open to sponsoring candidates currently in the U.S. who need to transfer their active visa. Please check with our Recruiting team if your visa is applicable for transfer.
*EvolutionIQ appreciates your interest in our company as a place of employment. EvolutionIQ is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At EvolutionIQ, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
EvolutionIQ AI Hiring
EvolutionIQ has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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