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About This Role
You desire impactful work.
You’re RGA ready
RGA is a purpose-driven organization working to solve today’s challenges through innovation and collaboration. A Fortune 200 Company and listed among its World’s Most Admired Companies, we’re the only global reinsurance company to focus primarily on life- and health-related solutions. Join our multinational team of intelligent, motivated, and collaborative people, and help us make financial protection accessible to all.
The AI Gateway Service Owner serves as the steward of the service, accountable for its strategy, financial performance, OKR attainment, OLA/SLA compliance, governance, and continuous business value realization.
The AI Gateway Service Owner is accountable end-to-end for the enterprise AI Gateway — from architecture through build, launch, and ongoing operation. This is a hybrid technical/operational leadership role: hands-on enough to make and defend architecture decisions, disciplined enough to run the gateway as a production service with SLAs, and strategic enough to build the team and roadmap that scale it across the enterprise.
Principle Duties
- AI Gateway Service Owner leads the end-to-end architecture, delivery, and operation of the AI Gateway, defining secure, scalable patterns for LLM traffic management, integrating with enterprise platforms, and guiding implementation through reusable standards and engineering oversight.
- The role is accountable for running the gateway as a production service, ensuring reliability, security, performance, cost efficiency, and adoption through well-governed operational practices and support models.
- Lead multiple complex projects and initiatives; manages related teams, sets organizational priorities, and allocates resources
- Develop and maintain effective business relationships, coordinating service delivery in line with negotiated Service Commitments.
- Collaborate with business partners to ensure properly defined and communicated project requests.
- Maintain current knowledge of industry practices, trends and applications.
- Perform other duties as assigned.
Education
- Bachelor’s Degree in Arts/Sciences (BA/BS) or equivalent experience - Required
- Master’s degree in Arts/Sciences (MA/MS) and/or LOMA certification - Preferred
Work Experience, Skills, and Abilities
- 8+ years in platform/infrastructure engineering, with 3+ years owning a production service end-to-end - Required
- 5+ years people management and team leadership experience - Required
- Demonstrated experience architecting and operating API gateways, service meshes, or similar middleware at enterprise scale - Required
- Working knowledge of LLM/AI infrastructure (model routing, inference platforms, prompt/token management, AI security concerns) - Required
- Track record building and leading technical teams - Required
- Fluency translating technical tradeoffs into business risk and OKRs for executive audiences - Required
- Experience supporting a global, multi-region service footprint with region-specific compliance obligations - Required
- Experience with zero-trust architecture and enterprise AI governance frameworks - Preferred
- Familiarity with CI/CD platforms, artifact registries, and on-prem AI inference deployment - Preferred
- Background in cyber risk quantification or security engineering - Preferred
- Hands-on implementation experience with one or more leading AI gateway platforms: Kong AI Gateway, Portkey, LiteLLM, Noma Security, Cloudflare AI Gateway, AWS Bedrock Gateway / Azure AI Gateway - Preferred
#LI-CW1
#LI-Hybrid
What you can expect from RGA:
- Gain valuable knowledge from and experience with diverse, caring colleagues around the world.
- Enjoy a respectful, welcoming environment that fosters individuality and encourages pioneering thought.
- Join the bright and creative minds of RGA, and experience vast, endless career potential.
We’re excited to get to know you and connect your unique skills with our global opportunities. To create a modern and seamless experience, we use artificial intelligence (AI) in parts of our preliminary screening process. This technology helps us personalize job recommendations, automate interview scheduling, evaluate candidates based solely on experience—without considering name, gender, or other personal details—and provide real-time answers through our chatbot. AI is used only during early screening and never makes hiring decisions. Your RGA recruiter will work closely with you every step of the way to ensure the process feels personal, thoughtful, and focused on you.
Compensation Range:
$126,710.00 - $188,840.00 Annual Base pay varies depending on job-related knowledge, skills, experience and market location. In addition, RGA provides an annual bonus plan that includes all roles and some positions are eligible for participation in our long-term equity incentive plan. RGA also maintains a full range of health, retirement, and other employee benefits.
RGA is an equal opportunity employer. Qualified applicants will be considered without regard to race, color, age, gender identity or expression, sex, disability, veteran status, religion, national origin, or any other characteristic protected by applicable equal employment opportunity laws.
Salary Context
This $126K-$188K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At RGA, 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($157K) sits 26% below the category median. Disclosed range: $126K to $188K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
RGA AI Hiring
RGA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Creve Coeur, MO, US. Compensation range: $188K - $188K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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