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About This Role
ABOUT US:
Ethos Risk Services is a leading insurance claims investigation and medical management company committed to providing better data that translates into better decision\-making for our clients. We are at the forefront of innovation in our space, and our success is driven by a dynamic team passionate about delivering exceptional services to our customers.
JOB SUMMARY:
Our synergetic Ethos IT Team is growing and seeking a full\-time Agentic AI Software Developer (REMOTE) to architect and guide software solutions in an AI\-first, spec\-driven environment. This role translates business requirements into structured specifications, oversees AI\-assisted development workflows, and ensures the quality, security, and performance of AI\-generated code.
Our ideal candidate is a senior engineer with hands\-on spec\-driven development experience who has worked with agentic AI tools and AI\-integrated systems. They have strong SQL experience, preferably PostgreSQL, and experience on development teams leveraging AI\-assisted workflows, including reviewing and validating AI\-generated code.
KEY RESPONSIBILITIES:
- Spec\-Driven Development– Translate business requirements into clear, testable specifications for AI\-assisted development.
- AI Workflow Orchestration – Oversee software generation using agentic AI tools, ensuring alignment with architecture.
- Code Review \& Quality – Rigorously review AI\-generated code for accuracy, security, and maintainability.
- Testing \& Validation – Ensure outputs meet specifications through strong testing practices.
- Prompt Engineering – Develop and refine prompts, constraints, and AI workflows for better outcomes.
- Architecture \& Modernization – Support scalable system design and migration from legacy environments.
- Risk \& Security – Identify and mitigate risks in AI\-generated code and ensure secure development practices.
- CI/CD \& Automation – Contribute to pipelines with validation and quality gates for generated code.
- Mentorship \& Support – Guide team members and provide production support for AI\-assisted systems.
- Documentation – Maintain clear specifications, workflows, and technical documentation.
- Collaboration – Partner with stakeholders to refine requirements and solution design.
QUALIFICATIONS:
Education: Master's degree in Computer Science, Data Science or a related field (preferred). Relevant experience may be a substitute for formal education.
Experience:
- Minimum of 7 to 10 years of software development experience, primarily with C\# and .NET technologies (required).
- Minimum of 5 years working with MS SQL Server and PostgreSQL (required).
- Minimum of 3 to 5 years of experience with cloud platforms, preferably in AWS (required).
- Hands\-on experience with AI\-assisted or spec\-driven development tools (e.g., Kiro or similar) (strongly preferred).
- Proven experience reviewing and validating AI\-generated code (required).
- Experience with modern front\-end frameworks, preferably in React.
- Strong background in CI/CD pipelines, automation, and quality assurance.
- Demonstrated ability to lead architecture and mentor engineering teams.
Certifications:
- AI\-assisted development or prompt engineering certifications (preferred).
- Microsoft, AWS, or security certifications (plus).
- Required completion of secure software development training annually, including AI\-specific secure coding practices and validation techniques.
Skills:
- Proficiency in Python and ML frameworks such as TensorFlow or PyTorch (preferred).
- Experience with MLOps, CI/CD pipelines, and infrastructure\-as\-code (Terraform or CloudFormation) (required).
- Strong understanding of data pipelines, model deployment, and performance monitoring (preferred).
- Ability to translate business requirements into scalable AI system designs (preferred).
- Excellent communication, stakeholder management, and problem\-solving skills (required).
WORKING CONDITIONS:
This is a primarily remote position working in a home office environment, with a preference for candidates able to work within Eastern or Central Time Zone business hours. The role requires prolonged periods of sitting or standing while operating a computer and other office equipment. Communication with team members, clients, and stakeholders is expected.
*Ethos Risk Services is an equal opportunity employer that does not discriminate on the basis of religious creed, sex, national origin, race, veteran status, disability, age, marital status, color or sexual orientation or any other characteristic.*
Salary Context
This $115K-$160K 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 Ethos Risk 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($137K) sits 36% below the category median. Disclosed range: $115K to $160K.
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.
Ethos Risk Services AI Hiring
Ethos Risk Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Raleigh, NC, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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