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About This Role
Overview:
As a global market leader, iPipeline combines technology, innovation, and expertise to deliver ground\-breaking, award\-winning software solutions that transform the life insurance, financial services, and protection industries. With one of the industry’s largest data sets, we help advisors/advisers and agents to transform paper and manual operations into a secure, seamless digital experience – from proposal to commission– so they can help better secure the financial futures of their clients.
At iPipeline, you’ll play a major role in helping us to provide best\-in\-class, transformative solutions. We’re passionate, creative, and innovative, and together as a team, we continually strive to advance, accelerate, and expand the reach of our technology. We value different perspectives and are committed to creating an environment that embraces diverse backgrounds and fosters inclusion.
We’re proud that we’ve been recognized as a repeat winner of various industry awards, demonstrating our excellence and highlighting us as a top workplace in both the US and the UK. We believe that the culture we’ve built for our nearly 900 employees around the word is exceptional \- and we’ve created a place where our employees love to come to work, every single day.
Come join our team! About iPipeline
Founded in 1995, iPipeline operates as a business unit of Roper Technologies (Nasdaq: ROP), a constituent of the Nasdaq 100, S\&P 500®, and Fortune 1000® indices. iPipeline is a leading global provider of comprehensive and integrated digital solutions for the life insurance and financial services industries in North America, and life insurance and pensions industries in the UK. We couple one of the most expansive digital and automated platforms with one of the industry’s largest data libraries to accelerate, automate, and simplify various applications, processes, and workflows – from quote to commission – with seamless integration. Our vision is to help everyone achieve lasting financial security by delivering innovative solutions that connect, simplify, and transform the industry.
iPipeline is proud to be an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to gender, race, color, religious creed, national origin, age, sexual orientation, gender identity, physical or mental disability, and/or protected veteran status*.* We are committed to building a supportive and inclusive environment for all employees. This is a hybrid position.
Responsibilities:
The Expert AI Engineer owns complex technical work within GenAI and AI platform services, designing and optimizing components of retrieval pipelines, inference systems, and evaluation frameworks. Applies strong debugging, performance engineering, and architectural judgment to improve system reliability and model grounding, while guiding less‑experienced team members and influencing team‑level technical decisions. GenAI \& AI Platform Development
- Design, implement, and optimize RAG pipelines, embeddings workflows, and LLM integration patterns.
- Contribute to scalable, low\-latency inference architecture across real\-time and batch pipelines supporting document processing, portfolio insights, and decision\-support use cases.
- Design ingestion, transformation, and indexing pipelines for vector stores and hybrid retrieval, including data curation processes and retrieval corpora in partnership with domain experts.
- Improve pipeline performance, reliability, integration quality, and cost\-efficiency across GenAI workflows.
- Design and maintain prompt templates, orchestration flows, and model configurations, establishing patterns for versioning, rollback, and auditability.
- Implement secure\-by\-design principles and contribute to responsible AI guidelines.
- Design and implement guardrail patterns (e.g., safety classifiers, content filters, policy checks) to mitigate harmful or non\-compliant outputs.
AI Evaluation \& Quality Engineering
- Design evaluation frameworks, datasets, and metrics to measure grounding, factuality, consistency, safety, and overall model quality.
- Build automated test harnesses and evaluation pipelines to support model iteration and validation.
- Analyze evaluation results and translate findings into actionable improvements to models and workflows.
- Apply grounding strategies and structured response patterns to reduce hallucinations and improve reliability.
Software Engineering \& System Design
- Lead the design and implementation of core GenAI system components and services.
- Participate in architectural discussions and propose improvements within the product area.
- Write modular, reusable, and maintainable code adopted across the team.
- Conduct code reviews, design reviews, and performance troubleshooting to ensure high\-quality, optimized systems.
- Mentor less\-experienced engineers on coding standards, testing practices, and system design.
Technical Competencies
- Strong software engineering background with hands\-on experience in AI/ML or LLM\-based systems.
- Deep experience with RAG architectures, embeddings pipelines, retrieval workflows, and LLM orchestration.
- Experience designing evaluation frameworks, datasets, and offline testing approaches.
- Proficiency with cloud\-native architectures, microservices, and containerization.
- Demonstrated commitment to high\-quality code, testing, documentation, and system reliability.
- Proven ability to mentor others and influence technical decisions within a team.
Qualifications:
- Typically requires 6\+ years of professional experience in AI/ML engineering, including ownership of model development and system integration.
Benefits:
We offer a competitive compensation and benefits package, opportunities for career growth, an employee stock purchase plan, 401(k), generous time off and flexible work/life balance, company\-matched retirement packages, an employee wellness program, and an awards and recognition program – all in a creative, fast\-growing, and innovative company.
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 4,021 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At iPipeline, 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 $180,000 based on 12,397 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
iPipeline AI Hiring
iPipeline has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Wayne, PA, US.
Location Context
Across all AI roles, 15% (608 positions) offer remote work, while 3,392 require on-site attendance. Top AI hiring metros: New York (2,585 roles, $210,300 median); San Francisco (2,102 roles, $253,000 median); Los Angeles (1,764 roles, $190,500 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 4,021 open positions tracked in our dataset. By seniority: 118 entry-level, 1,906 mid-level, 1,555 senior, and 442 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (608 positions). The remaining 3,392 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 4,021 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,818), Data Scientist (312), AI Software Engineer (280). 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 (118) are outnumbered by mid-level (1,906) and senior (1,555) 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 442 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (608 positions), with 3,392 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 $200,000. Top-quartile roles start at $253,000, 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 $290,000 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 (2,069 postings), Aws (1,260 postings), Azure (946 postings), Rag (893 postings), Gcp (783 postings), Pytorch (624 postings), Prompt Engineering (619 postings), Claude (570 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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