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About This Role
At Veracity, we aim to be a different kind of insurance partner – one that is free from outside investors, venture capital, or the pressures of a corporate parent.
Ours is a culture of empowerment – one that believes in effort, results, and accountability. We believe that transparency fosters trust, trust fosters growth, and that growth drives innovation. Our commitment to rigorous evaluation and relentless execution lead to rapid evolution.
We answer only to the small business owners we serve, and this independence allows us to stay focused on what matters most: helping their businesses thrive by providing expert guidance and best\-in\-class insurance policies.
We’re growing fast and want you to be a part of it!
We're seeking a technically sharp and organizationally influential Senior Applied AI Engineer to join our Engineering team. Veracity is building a new multi\-tenant, API/MCP\-first insurance platform where AI agents operate as first\-class principals alongside human users – built on a deterministic core with an agentic layer. Money movement, binding, and compliance run on deterministic, auditable systems; agents handle orchestration, intake, document understanding, and workflow acceleration on top.
This is not a chatbot\-building role. As a Senior Applied AI Engineer, you will do two jobs in one: build and harden production agent systems for a regulated insurance domain, and help define and implement our AI\-first engineering and product principles – how the whole organization designs, builds, ships, and operates software with AI in the loop. This is a senior individual\-contributor role with high architectural and organizational influence and a clear growth path toward technical leadership as the AI team scales.
Key Responsibilities
- Design and ship production agentic systems – multi\-agent orchestration, tool use, RAG pipelines, and MCP\-based integrations against platform APIs
- Build the reliability layer around agents – evals, guardrails, observability, cost controls, and regression testing – so agent behavior is measurable and defensible
- Implement maker\-checker patterns so AI accelerates regulated workflows without ever autonomously touching money, bind, or compliance decisions
- Integrate agent capabilities with the deterministic core including canonical data model, Config Hub, and Control Tower task orchestration
- Co\-author and implement our AI\-first engineering principles – agent harnesses, AI\-assisted code review, shared skills and prompt libraries, eval\-gated CI, and dev\-environment standards – then drive adoption across squads in Vilnius and the US
- Shape AI\-first product principles – where AI belongs in the product and where it doesn't, human\-in\-the\-loop patterns, agent UX conventions, and how AI capability estimates change scoping and build\-vs\-buy decisions
- Set the standard through your own delivery – demonstrating what AI\-first engineering makes possible and establishing the reference patterns other engineers adopt
- Work directly with engineering leadership on the AI architecture roadmap and year\-end production proof points
- Required to perform other duties as requested, directed, or assigned
Requirements and Qualifications
- 5\+ years of software engineering experience with strong backend or full\-stack foundations – APIs, event\-driven systems, and data modeling
- Real production experience with LLM\-powered systems – agent orchestration, tool calling, RAG, and structured outputs – not just prototypes or demos
- Demonstrated ability to make agentic systems reliable – evals, fallbacks, deterministic checkpoints, and cost and latency management
- Strong product judgment – you scope AI where it earns its place and default to deterministic code where it doesn't
- Evidence you've changed how others build, not just how you build – workflows, harnesses, or practices you introduced that a team adopted and kept using
- Clear written communication with comfort operating in an async\-first, distributed team environment
Perks
- Health, dental, and vision plans
- Amazing work\-life balance with 4 weeks of Paid Time Off
- 10 Paid Company Holidays with 2 floating holidays
- 401K Programs with employer match
- Personal assistance programs for support in a healthy personal and work life
Why Veracity?
Here at Veracity, you’ll be part of a team of trailblazers and visionaries. We’re not just revolutionizing the way people “do” insurance; we are creating a whole new paradigm. Here, you will experience a vibrant and inclusive workplace where your ideas matter! With us, you have a chance to:
- Engage in groundbreaking projects that are reshaping the insurance landscape
- Collaborate with a group of dedicated, like\-minded professionals
- Experience a culture that prioritizes growth and development
Compensation Range: $140k/yr \- $170k/yr
We are proud to be an equal\-opportunity employer. We are committed to providing equal opportunities to all qualified applicants, regardless of race, color, religion, sex, national origin, disability, or any other legally protected characteristics.
If you need accommodation, please let us know during the interview process.
Salary Context
This $140K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Veracity Insurance Solutions, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($155K) sits 28% below the category median. Disclosed range: $140K to $170K.
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.
Veracity Insurance Solutions AI Hiring
Veracity Insurance Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $170K - $170K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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