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About This Role
US citizens residing in US only.
About Us: Plexe LLC is a fast\-growing fintech lender transforming how small and medium\-sized businesses access working capital. Through our proprietary technology platform, automated underwriting, and data\-driven decision engine, we provide flexible revolving lines of credit that enable businesses to grow while delivering institutional\-grade credit performance.
As we continue to scale and expand our institutional funding partnerships, including digital and tokenized asset initiatives, data and artificial intelligence sit at the core of our next stage of growth.
We are seeking an exceptional Senior Data \& AI Officer (SDAO) for our data, analytics and artificial intelligence strategy. This executive will be responsible for ensuring the accuracy, integrity, and consistency of the data used in our underwriting models, decision rules, and automated approval processes. You will conduct thorough validation checks, implement testing automation, and provide end\-to\-end support for data flows in new model deployments. You will also be involved in building the next generation of credit intelligence, automation and predictive analytics across the business.
Position Overview:
The Senior Data \& AI Officer is responsible for the company's end\-to\-end data strategy, artificial intelligence initiatives, underwriting intelligence and analytical capabilities. Reporting directly to the Chief Executive Officer, the SDAO will oversee the design, governance and evolution of our data platform while driving innovation across credit decisioning, fraud detection, pricing, collections, customer experience and portfolio management.
This role combines executive leadership with hands\-on technical oversight, ensuring our proprietary lending platform continues to deliver industry\-leading automation, speed and credit performance.
Key Responsibilities:
- Data Validation: Verify the accuracy and consistency of data by comparing model variables, decision rules, and automated approval data against raw data sources.
- Data Integrity and Consistency Checks: Implement automated checks to continuously monitor the integrity and consistency of critical data, including loan application data, underwriting variables, credit risk metrics, and transaction data. Set up processes to detect and flag data anomalies, missing values, and outliers.
- Testing Automation and Tooling: Develop and utilize scripts, queries, or testing tools to automate data quality checks, ensuring efficient validation of large data sets used in underwriting and credit models. Maintain and update test cases for various data inputs, variables, and decision logic as new models or rules are developed.
- End\-to\-End Data Testing for Model Deployments: Participate in the testing phase of new model deployments to verify that model inputs and outputs are correct and aligned with raw data sources. Conduct post\-implementation testing to ensure that deployed models function as expected and data flows correctly from raw sources to decision outputs.
- Support for Model Calibration: Assist in calibrating models to ensure data accuracy and reliability as model parameters are updated or refined.
- Collaboration with Data Governance and Risk Teams: Work closely with data governance and risk management teams to ensure data quality aligns with the overall risk management strategy.
- Reporting \& Documentation: Document findings, errors, and validation processes. Prepare reports that summarize data quality issues and suggested improvements. Ensure data integrity across loan facilities, securitisation structures and tokenized investment products.
Qualifications:
- Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, Mathematics or a related discipline.
- Experience within fintech, lending, banking, payments or financial services is highly desirable.
- Minimum 3 years of experience in a data validation, quality assurance, or analytical role (internship or project experience considered).
- Strong analytical skills with attention to detail and accuracy.
- Familiarity with SQL and experience in querying and validating data.
- Basic understanding of model variables, decision rules, and automated decision\-making processes.
- Experience with Data Quality Tools: Familiarity with data quality and validation tools such as SQL Server Data Quality Services (DQS), DataRobot, Trifacta, or Talend for data cleansing and monitoring.
- Scripting and Automation Skills: Proficiency in Python or R for developing scripts to automate data validation and testing tasks.
- Strong documentation skills to create comprehensive reports on data quality issues, improvements, and testing results.
- Good communication skills, with the ability to work effectively in a remote team environment.
Preferred Qualifications:
- Familiarity with Microsoft Azure services or other cloud environments.
- Basic understanding C\#
- Basic understanding of model validation processes, especially if the role involves assisting with model testing and calibration.
- Experience with alternative credit data, open banking, financial APIs and automated decision engines will be highly regarded.
Why Join Us?
- Flexible Work Environment: Work from the comfort of your home with a flexible schedule aligned to US time zones.
- Learning and Growth: Be a part of a team that supports your professional development and career advancement in a fast\-growing company.
- Innovative Projects: Contribute to meaningful projects that make a difference in the financial industry.
- Equal Opportunity Employer: Plexe LLC is committed to creating a diverse and inclusive work environment. We are proud to be an equal opportunity employer and encourage applications from all backgrounds.
Job Types: Full\-time, Part\-time
Pay: $70,000\.00 \- $110,000\.00 per year
Experience:
- REST: 1 year (Preferred)
- Java: 1 year (Preferred)
Work Location: Remote
Salary Context
This $70K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Plexe LLC, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($90K) sits 59% below the category median. Disclosed range: $70K to $110K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Plexe LLC AI Hiring
Plexe LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $110K - $110K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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