Interested in this AI/ML Engineer role at BillGO, Inc.?
Apply Now →Skills & Technologies
About This Role
Senior AI Data Analytics Engineer
BillGO is building the next generation of payments — an intelligent network that helps small businesses get paid faster, operate leaner, and grow with confidence. The Senior AI Data Analytics Engineer sets the technical direction for BillGO's data and AI architecture, turning payments data into reliable, scalable, and intelligent products the rest of the organization builds on. Reporting to the VP, Data Office, this role sits at the intersection of data engineering, analytics, AI/ML, and the business. It's an individual\-contributor role with no direct reports — leadership is exercised through architecture, standards, and mentorship, not people management. Success is measured by the reliability, reuse, and trustworthiness of BillGO's data and AI products, not the volume of models or dashboards produced.
Why This Role Matters
BillGO's future runs on trustworthy data, and this role owns making sure it stays that way as the company scales. As the architect of the data models, semantic layers, and AI/RAG patterns that Product, Finance, Risk, and Operations all build on, this person turns scattered payments data into a single source of truth \- while setting the validation standards that keep AI\-generated insights accurate before they ever reach a decision\-maker. It's an individual contributor role with outsized reach: get it right, and BillGO moves faster with more confidence \- faster reconciliation, fewer fraud losses, and self\-service, AI\-powered insight in the hands of every team instead of just a few.
What You’ll Do
Data \& AI Architecture
- Own the architecture and roadmap for scalable data models covering customers, payments, transactions, settlements, and financial reporting
- Architect solutions across Snowflake, AWS RDS, and AWS DynamoDB, integrating sources from AWS S3
- Lead the design of data dictionaries, semantic layers, and data catalogs that power both human and AI\-driven analytics
Data Quality, Governance \& Standards
- Set and evangelize engineering standards, patterns, and best practices, and drive their adoption across the organization
- Establish frameworks for data quality and integrity through testing, monitoring, and documentation
- Support regulatory and financial reporting needs — reconciliation, audit readiness — with accurate, well\-governed data
Business Partnership \& Enablement
- Partner with senior leaders across Product, Finance, Risk, and Operations to define key metrics and enable insights, dashboards, and predictive models
- Translate ambiguous business strategy into data and AI solutions that scale with company growth
- Put AI\-powered, self\-service insights in the hands of every team
Technical Leadership \& Mentorship
- Set technical direction that improves visibility into payment performance and revenue drivers
- Mentor and coach engineers through design reviews, pairing, and code review
- Coach the team on using AI coding and analytics assistants to accelerate development and documentation
- How You'll Use AI
This role treats AI as core infrastructure, not a side project. You'll apply generative AI and large language models (e.g., Claude) to accelerate data transformation, documentation, and metric definition, and to enable natural\-language access to enterprise data. You'll architect retrieval\-augmented generation (RAG) and semantic search over enterprise data so trusted datasets are easily discoverable and queryable by both humans and AI systems. You'll design, build, and operationalize AI/ML workflows — from feature engineering to LLM\-powered pipelines — that turn analytics into predictions and automation. And because AI\-generated insight is only as good as its validation, you'll establish the responsible AI practices — around bias, hallucination, and data privacy — that ensure AI outputs are checked before they influence a financial decision. You'll also coach the broader team on using AI coding and analytics assistants to work faster and document better.
What You Bring
- 5\+ years in analytics engineering, data analytics, or data engineering, including senior or lead responsibilities
- Expert SQL and data analytics skills, with proven ability to model complex datasets (fact/dimension modeling, star schemas) and design data architecture end to end
- Deep experience with data warehousing (Snowflake) and transformation frameworks like Coalesce, including establishing team conventions
- Experience building and owning metrics layers or semantic models used across multiple teams
- Strong command of ELT pipelines, data orchestration, and Python for data processing and automation
- Extensive hands\-on experience applying generative AI and LLMs to real data and analytics problems in production
- Strong experience with RAG, embeddings, and vector databases, plus a solid ML and MLOps foundation
- A track record of technical leadership and mentorship, with a critical eye for data accuracy and AI\-generated results
- Payments, fintech, financial services, or enterprise SaaS experience strongly preferred
- Skill at influencing and communicating with senior technical and non\-technical stakeholders
- Nice to have: advanced data science/ML experience, LLM fine\-tuning or benchmarking, agentic AI workflows, event\-driven or streaming architectures, and hands\-on knowledge of payments concepts like authorization/settlement, interchange, chargebacks, and reconciliation.
Compensation
We offer a competitive executive compensation package, including:
- Base salary ($132,800 \- $196,500\)
- Performance incentive
- Equity opportunities
- Comprehensive health, retirement, and lifestyle benefits
This role is about more than compensation, it’s about the opportunity to transform how small businesses thrive in the digital economy.
Salary Context
This $132K-$196K range is below the median 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 BillGO, Inc., 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 ($164K) sits 25% below the category median. Disclosed range: $132K to $196K.
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.
BillGO, Inc. AI Hiring
BillGO, Inc. has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Fort Collins, CO, US. Compensation range: $146K - $196K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.