More than a mission, C2FO is a better financial system changing the way every business gains access to the working capital they need to thrive. At C2FO, everyone is an employee-owner which means we’re all invested in our work and team members. We’re a company of team players and self-starters finding new and innovative ways to get things done. If you’re excited to learn, grow, and leave your mark on our fast-growing organization, C2FO may be the place for you.
About C2FO
Headquartered in Kansas City, USA, C2FO has more than 500 employees worldwide, with operations throughout North America, Europe, India, Asia Pacific, and Australia. C2FO is the world’s largest on-demand working capital platform. Our mission is to ensure every business has the capital needed to thrive and we have delivered more than $400+ billion in funding to businesses since our founding. How do we do this? By providing fast, flexible, and equitable access to low-cost capital through our easy-to-use platform.We provide technology with a human touch, giving our customers the direct support they need and ensuring our team members have the tools, resources, and work environment they need to deliver on our promise to customers. With the C2FO platform, businesses worldwide have more working capital to fuel their growth, create jobs and develop new products.
Benefits
At C2FO, we take care of our customers and our people – the vital human capital that helps our customers thrive. That’s why we offer a comprehensive benefits package, flexible work options for work/life balance, volunteer time off, and more. Learn more about our benefits here. (https://www.c2fo.com/amer/us/en-us/about-us/careers
Designation: Applied AI Engineer, GenAI and ML Prototyping
Experience: 4-6 years
Location: REMOTE
Role OverviewWe are looking for an Engineer/Data Scientist to lead the identification and rapid prototyping of AI solutions across our business — spanning both internal operations and customer-facing products.
This role sits at the earliest and most critical stage of our AI delivery lifecycle: Discovery and Proof of Concept. You will partner with Senior and Principal engineers and work directly with department heads and product owners to uncover where AI can create meaningful impact, then design and build working prototypes that demonstrate clear, measurable value. You will own the process from problem framing through to a validated, decision-ready POC — determining whether the right solution is a rule-based system, a traditional machine learning model, or an LLM-based agentic workflow.
Once a prototype is approved, you will work in close collaboration with the rest of the AI Platform Engineering team to translate your work into something that can scale into a production-grade application. You will not co-own productionisation and you will be a critical partner in making it successful.
This is a role for someone who is energised by ambiguity, moves fast without cutting corners, and knows how to make a compelling case for (or against) a technical approach based on evidence rather than enthusiasm.
Core Responsibilities
- Business Discovery Run structured discovery sessions with department heads and product owners to identify and scope AI opportunities. Define a clear problem statement — including data availability and constraints — before any prototyping begins.
- Rapid Prototyping Build functional POCs using the most appropriate approach for the problem: RAG pipelines, agentic workflows, predictive ML models, or rule-based systems. Prototypes must be credible enough to support a genuine build-or-not decision.
- Stakeholder Management Act as the primary technical point of contact for business stakeholders throughout discovery and POC. Communicate trade-offs around accuracy, cost, and latency in plain terms — and be willing to recommend against building when the evidence calls for it.
- Evaluation & Validation Define success criteria before building begins. Design and run evaluations appropriate to the POC type, and present findings clearly enough for a non-technical sponsor to make a confident go/no-go decision.
- Technical Handoff Produce handoff documentation covering system design, prompt strategies, data requirements, known failure modes, and evaluation benchmarks — giving the AI Engineering team everything needed to take a validated POC into production.
Proficiency in Python as the primary language for data science and ML development (Pandas, NumPy, Scikit-learn)
Familiarity with SQL for data querying and manipulation across modern data warehouses (e.g., BigQuery, Snowflake, PostgreSQL)
(Nice to have) Working knowledge of deep learning frameworks such as PyTorch or TensorFlow for model experimentation
Hands-on experience working with large language model APIs, including providers such as OpenAI, Anthropic, or Google
Strong command of prompt engineering techniques, including few-shot prompting, chain-of-thought reasoning, and structured output design
Experience with open-source LLMs (e.g., Mistral, LLaMA) and an understanding of when to apply open vs. proprietary models
Practical experience building RAG (Retrieval-Augmented Generation) pipelines, including chunking strategies, embedding models, and retrieval tuning
Familiarity with agentic orchestration frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen
Experience integrating vector databases (e.g., pgvector, Pinecone, Weaviate, ChromaDB) into search and retrieval workflows
Understanding of tool/function calling patterns for LLM-driven automation
Ability to define and implement "good enough" metrics and evaluation frameworks for POC validation
Experience with LLM evaluation libraries such as RAGAS, TruLens, or DeepEval
Familiarity with experiment tracking tools such as MLflow or Weights & Biases
Comfort with cost and latency profiling of LLM-based systems to inform feasibility decisions
Comfortable working within cloud environments (AWS, GCP, or Azure) for data access, compute, and API integration
Ability to integrate with REST APIs and third-party data sources during prototyping
Proficiency with standard development tools: Git, Jupyter notebooks, VS Code
Basic familiarity with Docker for packaging and sharing POC environments with engineering teams
4+ years of experience in data science, machine learning, or a closely related field, with a demonstrated track record of delivering end-to-end projects
2+ years of hands-on experience working with large language models or Generative AI solutions in a professional setting
Proven experience taking projects from business problem discovery through to a working prototype or proof of concept
Experience engaging directly with non-technical business stakeholders to gather requirements, set expectations, and communicate results clearly
Strong background in traditional ML approaches (classification, regression, clustering, NLP) alongside modern LLM-based methods
Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
A Master's or PhD is a plus, though equivalent industry experience is equally valued
Ability to translate complex technical outputs into clear business value — you are as comfortable in a boardroom as you are in a notebook
Strong stakeholder management skills, including the ability to set realistic expectations around LLM capabilities, limitations, and cost trade-offs
Excellent written communication skills for documenting prompt strategies, data requirements, and POC logic to enable clean technical handoffs
Self-directed with a high tolerance for ambiguity — you are energised by open-ended discovery, not slowed down by it
Structured thinker who can design evaluation criteria and define what "success" looks like before building begins
Experience with fine-tuning or instruction-tuning LLMs on domain-specific datasets
Familiarity with responsible AI principles, including bias detection, fairness evaluation, and model transparency
Prior experience in a consulting, pre-sales engineering, or business-facing technical role
Knowledge of business process mapping (e.g., BPMN) to support structured discovery sessions
Skills Required
- 4+ years of experience in data science, machine learning, or a closely related field
- 2+ years of professional experience working with large language models or Generative AI solutions
- Demonstrated experience delivering end-to-end projects from business problem discovery through working prototype or proof of concept
- Experience engaging directly with non-technical business stakeholders to gather requirements, set expectations, and communicate results
- Strong background in traditional machine learning, including classification, regression, clustering, and NLP
- Proficiency with Python, Pandas, NumPy, and Scikit-learn
- Familiarity with SQL and modern data warehouses such as BigQuery, Snowflake, or PostgreSQL
- Hands-on experience with large language model APIs
- Strong command of prompt engineering techniques
- Experience building RAG pipelines, including chunking, embeddings, and retrieval tuning
- Familiarity with agentic orchestration frameworks
- Experience integrating vector databases into search and retrieval workflows
- Ability to define evaluation metrics and frameworks for proof-of-concept validation
- Experience with LLM evaluation libraries such as RAGAS, TruLens, or DeepEval
- Familiarity with experiment tracking tools such as MLflow or Weights & Biases
- Experience profiling cost and latency of LLM-based systems
- Comfort working in AWS, GCP, or Azure cloud environments
- Ability to integrate REST APIs and third-party data sources
- Proficiency with Git, Jupyter notebooks, and VS Code
- Basic familiarity with Docker
- Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
- Master's degree or PhD
- Experience with PyTorch or TensorFlow
- Experience with open-source LLMs such as Mistral or LLaMA
- Experience fine-tuning or instruction-tuning LLMs
- Familiarity with responsible AI principles, bias detection, fairness evaluation, and model transparency
- Consulting, pre-sales engineering, or business-facing technical experience
- Knowledge of business process mapping such as BPMN
C2FO Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about C2FO and has not been reviewed or approved by C2FO.
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Healthcare Strength — Health coverage for employees is described as fully paid for medical, dental, and vision in the U.S., with company‑paid life insurance and long‑term disability. This reduces typical out‑of‑pocket premiums and signals a strong medical benefits foundation.
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Leave & Time Off Breadth — Time‑off provisions include unlimited PTO, paid volunteer time, flexible hours/remote options, and a paid sabbatical after 10 years. These policies expand usable time away and flexibility beyond standard packages.
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Parental & Family Support — Parental leave is described as fully paid, with mentions of fertility support. These elements strengthen support for families alongside core medical coverage.
C2FO Insights
What We Do
Our mission is to deliver a future where every company in the world has the capital needed to grow. C2FO's convenient, easy-to-use platform enables companies to work with their suppliers and customers to improve cash flow by determining the best time and terms for paying or getting paid. Today, C2FO is the world’s largest market for working capital. We serve over one million businesses representing $10.5 trillion in annual sales across 183 countries. Our online platform connects more than $110 billion of daily accounts payable and accounts receivable. By providing cost-effective access to working capital, we’re bringing fairness, transparency and, above all, opportunity to the world’s growing businesses. Because we believe that when every business has a fair chance to thrive, we all benefit.
