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About This Role
Are you looking to make an impactful difference in your work, yourself, and your community? Why settle for just a job when you can land a career? At ICW Group, we are hiring team members who are ready to use their skills, curiosity, and drive to be part of our journey as we strive to transform the insurance carrier space. We're proud to be in business for over 50 years, and its change agents like yourself that will help us continue to deliver our mission to create the best insurance experience possible.
Headquartered in San Diego with regional offices located throughout the United States, ICW Group has been named for ten consecutive years as a Top 50 performing P\&C organization offering the stability of a large, profitable and growing company combined with a focus on all things people. It's our team members who make us an employer of choice and the vibrant company we are today. We strive to make both our internal and external communities better everyday! Learn more about why you want to be here!
PURPOSE OF THE JOB
The Applied AI Engineer is responsible for developing, deploying, and maintaining applied generative AI solutions that support the organization’s insurance products, internal workflows, and customer experiences. This role focuses on building reliable AI models and pipelines, collaborating closely with cloud engineering, AI, and ML Ops teams, and implementing best practices for model performance, security, and cost efficiency. The engineer will contribute to AI initiatives while developing expertise in generative AI, cloud deployment, and feature engineering using Snowflake.
ESSENTIAL DUTIES AND RESPONSIBILITIES
- Implement end\-to\-end generative AI solutions, including model fine\-tuning, deployment, and testing in production environments.
- Collaborate with cloud engineering, AI, and ML Ops teams to operationalize AI workloads on AWS using services such as SageMaker, Lambda, ECS/EKS, and S3\.
- Build and maintain AI/ML pipelines leveraging Snowflake for feature engineering and data preprocessing.
- Follow organizational security and compliance standards when developing and deploying AI solutions.
- Monitor model performance, troubleshoot issues, and implement optimizations for reliability and cost efficiency.
- Participate in research and evaluation of generative AI technologies to inform project decisions.
- Document AI models, pipelines, and processes for team knowledge sharing and compliance.
- Support senior engineers in AI architecture reviews, code reviews, and operationalization planning.
EDUCATION AND EXPERIENCE
- Bachelor’s degree in Computer Science, Data Science, Applied Mathematics, or a related technical discipline.
- 3\+ years of experience in AI/ML engineering, with at least 1–2 years focused on generative AI or large language models.
- Hands\-on experience deploying AI/ML models in cloud environments, preferably AWS.
- Familiarity with Snowflake for AI/ML feature engineering and data integration.
- Experience in regulated industries or highly data\-sensitive environments is a plus.
CERTIFICATES, LICENSES, AND REGISTRATIONS
- AWS Certified Machine Learning – Specialty or AWS Certified Solutions Architect is preferred but not required.
- Optional AI/ML certifications (e.g., TensorFlow Developer, Hugging Face Course, or Generative AI specialization) are a plus.
- Awareness of compliance and governance standards (SOC 2, ISO 27001\) is beneficial.
KNOWLEDGE AND SKILLS
- Understanding of generative AI architectures, including transformers and retrieval\-augmented generation.
- Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers.
- Knowledge of MLOps practices, including CI/CD pipelines, testing, and monitoring.
- Familiarity with containerization (Docker, Kubernetes) and deployment in cloud environments.
- Solid understanding of data pipelines, Snowflake architecture, and ETL/ELT best practices.
- Awareness of security, governance, and compliance considerations for AI/ML systems.
- Ability to troubleshoot model performance and optimize workloads for efficiency and cost.
- Strong problem\-solving and collaboration skills, with effective communication to both technical and non\-technical stakeholders.
SUPERVISORY RESPONSIBILITIES
This position has no supervisory responsibility but may mentor and train junior engineers.
PHYSICAL REQUIREMENTS
Office environment – no specific or unusual physical or environmental demands and employees are regularly required to sit, walk, stand, talk, and hear. Employees are required to reach with hands and arms; stoop, kneel, crouch, or crawl. Employees must occasionally lift and/or move up to 30 pounds. Employees are required to have visual acuity and be capable of operating and viewing computers and other electronic devices for extended periods of time.
WORK ENVIRONMENT
This position operates in an office environment and requires the frequent use of a computer, telephone, copier, and other standard office equipment. This description is a general statement of essential job functions and responsibilities. The position may include other duties as assigned.
We are currently not offering employment sponsorship for this opportunity.
\#LI\-TM1 \#LI\-HYBRID
The current range for this position is
$121,624\.81 \- $217,710\.99
This range is exclusive of fringe benefits and potential bonuses. If hired at ICW Group, your final base salary compensation will be determined by factors unique to each candidate, including experience, education and the location of the role and considers employees performing substantially similar work.
WHY JOIN ICW GROUP?
- Challenging work and the ability to make a difference
- You will have a voice and feel a sense of belonging
- We offer a competitive benefits package, with generous medical, dental, and vision plans as well as 401K retirement plans and company match
- Bonus potential for all positions
- Paid Time Off
- Paid holidays throughout the calendar year
- Want to continue learning? We’ll support you 100%
*ICW Group is committed to creating a diverse environment and is proud to be an Equal Opportunity Employer. ICW Group will not discriminate against an applicant or employee on the basis of race, color, religion, national origin, ancestry, sex/gender, age, physical or mental disability, military or veteran status, genetic information, sexual orientation, gender identity, gender expression, marital status, or any other characteristic protected by applicable federal, state or local law.*
At ICW Group we offer a work environment that encourages entrepreneurialism and celebrates success. Our team members are hands\-on contributors who are given the opportunity to make an impact. It's our people who make us an employer of choice and the vibrant company we are today.
Job Category: IT
Job Type: Full time
Req ID: JR101459
Salary Context
This $121K-$217K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At ICW Group, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($169K) sits 6% below the category median. Disclosed range: $121K to $217K.
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.
ICW Group AI Hiring
ICW Group has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $189K - $217K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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