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About This Role
AI Solutions Architect – Quality Strategy
Remote \| Anywhere in the U.S.
We're looking for a strategic leader to drive our Quality\-at\-Source vision and transform how quality is embedded across engineering teams.
Lead quality standards, tooling, and best practices across the organization
Champion Test\-Driven Development (TDD) and Behavior\-Driven Development (BDD)
Drive the shift from reactive QA to a proactive, AI\-powered development lifecycle
Define verification \& validation (V\&V) standards for AI\-driven applications
Partner with engineering leaders to build a culture of quality and continuous improvement
✅ Experience in software quality strategy, test automation, and engineering leadership
✅ Strong background in TDD, BDD, and modern software delivery practices
✅ Knowledge of quality frameworks for AI/ML applications
Join us and help shape the future of AI\-driven quality engineering.
- Strategic Engineering Leadership
+ Process Transformation: Lead the design and conversion of legacy quality assurance processes into a modern, continuous improvement framework with shift\-left testing and validation.
+ TDD/BDD Implementation: Establish, evangelize, and implement TDD and BDD methodologies across the entire application portfolio to ensure code is testable and requirements are executable.
+ V\&V Governance: Own the ultimate validation and verification of the SDLC, ensuring that quality is baked into the CI/CD pipeline and local development environments.
- AI Validation Focus
+ AI Verification: Design and implement concrete validation pipelines for AI\-assisted development outputs, including:
- Prompt engineering standards — Establish reusable prompt templates and guardrails for agentic code generation, with version\-controlled prompt libraries.
- Output validation gates — Build automated checks that evaluate AI\-generated code against security, style, and correctness baselines before it enters the review cycle.
- Non\-deterministic testing frameworks — Develop assertion strategies for AI outputs where exact results vary (e.g., confidence\-scored evaluations, boundary testing, regression suites against known\-good outputs).
- Human\-in\-the\-loop checkpoints — Define which categories of AI output require manual review (UX decisions, business logic, security\-sensitive code) and build the workflow tooling to surface them efficiently.
+ Intelligent Tooling: Own an evaluation\-driven roadmap for AI developer tools. Each tool adoption must include a measurable hypothesis (e.g., “AI\-assisted test generation reduces test authoring time by 40% within 90 days”) with a defined pilot, measurement period, and go/no\-go criteria before org\-wide rollout.
+ Agentic Engineering Coaching: Develop and deliver training programs that teach development teams to treat AI agents as junior developers — validating outputs, writing effective prompts, and designing workflows where AI acceleration doesn’t bypass quality gates.
- Culture \& Influence
+ Diplomatic Change Management: Use persuasion and diplomacy to bridge gaps between product, engineering, and operations, moving the organization toward a collaborative "Quality First" mindset.
+ Thought Leadership: Leading at the company as a pioneer in AI\-driven quality engineering and software validation in general.
- Product Owner Partnership
+ TDD and BDD are only as effective as the requirements they validate. This role must have an explicit mandate to partner with — and push back on — product ownership:
+ Acceptance criteria quality standards — Define what “good enough to build against” looks like. Work with product managers to ensure stories include testable acceptance criteria before engineering begins work.
+ Requirements readiness gate — Authority to flag and return insufficiently specified work to product before it enters a sprint. If requirements are garbage in, quality will be garbage out regardless of test automation.
+ BDD collaboration model — Establish a structured process where product, engineering, and QA co\-author behavioral specifications (Given/When/Then) before development begins, ensuring shared understanding of “correct.”
- Advanced Degree in related field required
- Minimum of seven years of related experience is required.
- Prior management/supervisory experience is required.
- Technical Expertise
+ Development Roots: A strong background in software development (e.g., Java, Python, or C\#, node ecosystem) with a genuine passion for the art of code verification.
+ SDLC Mastery: Deep experience in building and optimizing CI/CD pipelines and local developer workflows.
+ Methodology Expert: Proven track record of successfully deploying TDD and BDD at an enterprise scale.
+ Agentic Engineering: Strong experience in AI assisted development with an emphasis on building validation into the AI generation.
- Leadership Traits
+ Results\-Oriented: A focus on metrics that matter (e.g., Lead Time, Change Failure Rate, and Mean Time to Recovery).
+ The "Diplomatic Architect": Ability to influence senior stakeholders and mentor junior engineers simultaneously.
+ Continuous Learner: Obsessed with the evolving landscape of AI and engineering tooling.
- ISTQB Test Manager or ASQ Certified Software Quality Engineer (CSQE). Preferred
- Project Management Professional (PMP) or Certified Scrum Master (CSM). Preferred
- AWS Certified Developer. (Preferred)
\#LI\-DV1
Why Crawford?
Because a claim is more than a number \- it’s a person, a child, a friend. It’s anyone who looks to Crawford on their worst days. And by helping to restore their lives, we are helping to restore our community \- one claim at a time.
At Crawford, employees are empowered to grow, emboldened to act and inspired to innovate. Our industry\-leading team pioneers new solutions for the industries and customers we serve. We’re looking for the next generation of leaders to take this journey with us.
We hail from more than 70 countries and speak dozens of languages, reflecting the global fabric of the audience we serve. Though our reach is vast, we proudly operate as One Crawford: united in purpose, vision and values. Learn more at www.crawco.com.
When you accept a job with Crawford, you become a part of the One Crawford family. And as part of the One Crawford family, we offer a comprehensive Total Rewards package to our employees to assist with their financial, health/wellness, continuing education/training and other needs:
- Competitive base pay
- The Pay Range/Salary represents the anticipated low and high pay that may be offered for this position. To determine the actual offer, Crawford considers a wide range of factors including the candidate’s previous experience and education, market rates, minimum pay requirements for the applicable jurisdiction, business segment, supply/demand, and scheduled hours.
- Bonus/Incentive Pay and other Performance\-Based Rewards, if applicable.
- We offer a well\-rounded benefits package that encourages wellness and helps our employees to be an educated healthcare consumer. The core benefits\* offered include:
- Medical, Dental and Vision Plans
- Prescription Drugs
- HSA, HRA, and FSA Accounts
- Paid Holidays, Vacation and Sick Leave
- 401(k) Retirement Plan
- Tuition Assistance
- Paid Parental Leave
- Supplemental Health Benefits
Other Benefits currently available at no cost include:
- Enhanced Mental Health Support
- Virtual Physical Therapy
- Caregiving Services
- Life Assistance Program
- The above information highlights some of the benefits currently available to eligible full\-time employees.
- Training programs that promote continuous learning and career progression while enhancing job performance.
- Sustainability programs that give back to the communities in which we live and work.
- A culture of respect, collaboration, entrepreneurial spirit and inclusion.
Crawford \& Company participates in E\-Verify and is an Equal Opportunity Employer. M/F/D/V Crawford \& Company is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at Crawford via\-email, the Internet or in any form and/or method without a valid written Statement of Work in place for this position from Crawford HR/Recruitment will be deemed the sole property of Crawford. No fee will be paid in the event the candidate is hired by Crawford as a result of the referral or through other means.
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 Crawford & Company, 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. 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.
Crawford & Company AI Hiring
Crawford & Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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