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About This Role
At Alliant Insurance Services, we thrive on creating employee benefits solutions built on the idea that health makes growth possible. As top tier professionals dedicated to solving our clients' health and welfare insurance challenges, Alliant team members deliver an unrivaled depth of service, and our unique approach enables us to help clients stay resilient and turn change into opportunity. This position is a chance to join a dynamic, expanding company with prospects for individual and career growth.
As one of the 5 largest insurance brokerage firms in the U.S., Alliant provides property and casualty, workers' compensation, employee benefits, surety, and financial products and services to clients nationwide.
More information is available at www.alliant.com.
SUMMARY
Responsible for leading a substantial technical domain within the AI platform and makes it dependable for use across multiple products. Independently translates ambiguous needs into architecture and staged delivery, then remains accountable for operational performance and adoption. Raises engineering quality through technical guidance, mentorship, and practical standards for shared AI systems.
ESSENTIAL DUTIES AND RESPONSIBILITIES
Owns architecture, implementation, and operational health for a platform domain, such as agent lifecycle and state, sandbox context exchange, model orchestration, or tracing and evaluation.
Designs services and interfaces that support long-running tasks across deployments, restarts, retries, inspection, and replay, with appropriate durability and failure recovery.
Establishes reusable patterns for context packaging and retrieval across execution environments, including clear boundaries for access, retention, and data movement.
Builds or evolves model routing and orchestration capabilities; defines measurements for quality, latency, reliability, and cost per task and uses them to guide improvements.
Leads technical discovery with product teams, identifies common needs, and prioritizes platform work that reduces duplicated engineering and unplanned interruptions.
Defines domain-level testing, tracing, evaluation, and operational readiness practices; reviews production signals and leads resolution of complex incidents in the domain.
Produces architecture documentation and migration guidance that enable engineers to adopt platform capabilities without extensive one-to-one support.
Mentors engineers, reviews designs and code, and contributes to technical planning across the AI engineering group.
Performs all duties in accordance with all company policies and procedures, and all federal, state and local laws, wherein the Company operates.
Performs other duties as assigned.
QUALIFICATIONS
EDUCATION/EXPERIENCE
Bachelor's Degree in Computer Science, Software Engineering, a related technical field, or equivalent combination of education and work experience.
Five (5) or more years of software engineering experience, or equivalent practical experience, including ownership of production services.
Experience building LLM-powered applications or AI infrastructure.
SKILLS
Advanced proficiency in TypeScript and/or Python and experience designing production services and APIs
Knowledge of distributed systems, asynchronous job processing, persistence, observability, cloud deployment, and secure system design
Experience shipping and supporting LLM features, including practical tradeoffs among model behavior, latency, and cost
Ability to lead technical work from ambiguous problem definition through design, implementation, rollout, and operational improvement
Ability to make platform interfaces reusable and understandable across teams with different product needs
Ability to influence technical decisions, communicate risk and tradeoffs, and mentor engineers across experience levels
#LI-AS1
#LI-REMOTE
We are proud to provide comprehensive, high quality employee programs to meet employees' needs now and in the future, including a very competitive financial package. We encourage you to explore what we have to offer.
For immediate consideration for this position, please click on the "Apply" button.
Alliant Insurance Services, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment based on their qualifications and ability without regard to race, color, religion, sex (including pregnancy, childbirth, breastfeeding, sexual orientation, and gender identity), national origin, ancestry, physical or mental disability, medical condition, marital status, age, genetic information, or status as a protected veteran, in accordance with applicable federal, state, and local laws, including California law.
Applicants are protected under Federal law from discrimination. If you need an accommodation to complete the application process or would like to review these materials in an alternative format, please reach out to [email protected]
For more information on Alliant Insurance Service's benefits, please visit www.alliant.com/about/careers/benefits
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Alliant Insurance Services, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Alliant Insurance Services AI Hiring
Alliant Insurance Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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