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About This Role
About Luma Financial Technologies
Founded in 2018, Luma Financial Technologies (“Luma”) has pioneered a cutting\-edge fintech software platform that has been adopted by broker/dealer firms, RIA offices, and private banks around the world. By using Luma, institutional and retail investors have a fully customizable, independent, buy\-side technology platform that helps financial teams more efficiently learn about, research, purchase, and manage alternative investments as well as annuities. Luma gives these users the ability to oversee the full, end\-to\-end process lifecycle by offering a suite of solutions. These include education resources and training materials; creation and pricing of custom structured products; electronic order entry; and post\-trade management. By prioritizing transparency and ease of use, Luma is a multi\-issuer, multi\-wholesaler, and multi\-product option that advisors can utilize to best meet their clients’ specific portfolio needs. Headquartered in Cincinnati, OH, Luma also has offices in New York, NY, Miami, FL, Zurich, Switzerland and Lisbon, Portugal. For more information, please visit Luma’s website.
About the role
Luma Fintech is building a best\-in\-class LLM\-powered document parsing pipeline that extracts structured data from complex financial product term sheets. We are seeking an AI Quality Engineer to own the daily testing, analysis, and iterative improvement of our Claude API\-based extraction system. This role sits at the intersection of financial data operations and applied AI, you will be the person who closes the loop between what the model outputs and what the schema demands.
What you'll do
- Run daily accuracy evaluations against a defined extraction schema, tracking field\-level performance across structured product types (autocallables, CLNs, barrier notes, etc.)
- Design and maintain test cases, regression suites, and gold\-standard document sets to benchmark extraction quality over time
- Diagnose extraction failures, distinguishing between prompt logic issues, schema ambiguity, model hallucinations, and edge\-case document formats
- Iterate on prompt engineering, system instructions, and context design to improve field\-level extraction accuracy
- Work alongside the AI Engineer lead to feed findings into validation logic and rules\-based layers that sit on top of LLM output
- Document failure modes with reproducible examples and root\-cause hypotheses
- Build and maintain evaluation metrics (precision, recall, field coverage, hallucination rate) and report on accuracy trends
- Flag schema gaps or ambiguities surfaced by real document variance and collaborate with data operations to refine field definitions
- Contribute to RAG improvements by identifying where retrieved context is insufficient or misleading
Qualifications
Required
- Hands\-on experience working with LLM APIs (Anthropic, OpenAI, or similar) in a production or near\-production context
- Strong prompt engineering skills, you understand how instruction design affects model behavior, not just output tone
- Analytical mindset with the ability to systematically isolate variables in model output quality
- Experience designing structured test cases or evaluation frameworks (QA background is a plus)
- Familiarity with JSON schema, structured data output, and data validation patterns
- Ability to read and interpret complex financial or legal documents (term sheets, prospectuses, offering documents), prior financial services exposure strongly preferred
- Strong written communication; you’ll be documenting findings for both technical and non\-technical stakeholders
Preferred
- Experience with RAG pipelines and retrieval evaluation
- Python proficiency for scripting evaluation workflows or parsing outputs
- Background in structured financial products (autocallables, structured notes, credit\-linked notes)
- Familiarity with evaluation frameworks or tools (e.g., LangSmith, RAGAS, custom evals)
What Success Looks Like
In 90 days, you have established a repeatable daily evaluation process, a documented baseline of field\-level accuracy across product types, and have driven at least one measurable improvement in extraction quality through prompt iteration.
Why This Role
This is a high\-ownership position on a strategic automation initiative with direct visibility to leadership. You won’t be maintaining someone else’s test suite, you’re building the quality layer of a system that processes real financial data at scale. The role will evolve as the system matures, with opportunity to expand into evaluation infrastructure and model improvement strategy.
The pay range for this role is:
90,000 \- 115,000 USD per year(Cincinnati)
Salary Context
This $90K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2088 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 4,021 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Luma Financial Technologies, 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. This role's midpoint ($102K) sits 43% below the category median. Disclosed range: $90K to $115K.
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.
Luma Financial Technologies AI Hiring
Luma Financial Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cincinnati, OH, US. Compensation range: $115K - $115K.
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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