Staff Engineer - AI

BillGO, Inc.•Fort Collins, CO

About The Position

Staff AI Engineer: Shape the future of payments with AI BillGO is building the next generation of payment and money movement infrastructure for small businesses. AI is core to how we scale reliability, reduce operational friction, and deliver better outcomes across payments, risk, and support. We’re hiring a Staff AI Engineer, a senior individual contributor who combines deep hands-on engineering with strong technical leadership. This role reports directly to the CTO and operates as a trusted technical partner to senior leadership, helping turn business intent into production-grade AI systems that operate at scale. What You’ll Do Build AI Where It Matters Design and ship AI/ML systems embedded directly in B2B payment flows, including: Payment prioritization and acceleration Cash-flow forecasting and predictive insights Automated reconciliation, exception handling, and workflow orchestration Balance accuracy, latency, explainability, reliability, and cost in business-critical systems. Own model behavior in real-world production environments, not just offline metrics. Multiply the Organization with AI Partner with Product, Engineering, Operations, and Finance to: Automate internal workflows using ML and LLMs Replace manual reviews and heuristics with intelligent systems Reduce cost-to-serve while increasing throughput and quality Build AI tools and platforms that allow small teams to operate at scale. Technical Leadership & Ownership Own the end-to-end lifecycle of AI systems: problem framing, architecture, data and feature design, deployment, monitoring, and continuous improvement. Define architectural direction for AI-enabled platforms and workflows spanning multiple teams and domains. Act as a senior technical leader and force multiplier, providing clarity, judgment, and direction across concurrent initiatives. Evaluate and adopt AI, data, and automation technologies where they deliver measurable business value. Influence execution through technical leadership rather than formal authority. Applied AI, ML Ops & Architecture Build production-grade AI systems embedded in business-critical operational workflows (e.g., payments, risk review, support triage). Design decision systems combining rules, ML inference, and self-healing capabilities. Operate and evolve ML infrastructure including: Model serving and inference pipelines Feature engineering and online/offline consistency Monitoring for data quality, model performance, and system health Work with modern cloud-native architectures: event-driven systems, streaming pipelines, and real-time processing. Make informed build-vs-buy decisions for AI and data platforms. Trust, Reliability & Fintech Rigor Design AI systems that meet the demands of regulated financial environments. Ensure security, privacy, auditability, and explainability of AI-driven decisions. Implement safe deployment practices such as shadow mode, canary releases, back testing, and rollback. Proactively identify and mitigate risks related to bias, failure modes, and unintended system behavior.

Requirements

10+ years of professional software engineering experience, with significant ownership of production systems.
Proven experience shipping AI / ML enabled systems into real-world production.
Strong proficiency in one or more backend languages (Java, C#, Python).
Deep understanding of distributed systems and modern architectural patterns.
Experience with cloud platforms (AWS and/or Azure), microservices, and event-driven systems.
Hands-on experience with CI/CD, containerization (Docker), and Kubernetes.
Strong understanding of production ML systems, including: Model inference and serving Feature engineering and data quality Monitoring and operating ML models (MLOps)
Comfortable operating in ambiguity and making decisions with imperfect information.
Strong technical judgment with a bias toward action.
Clear communicator who can influence both technical and non-technical partners.
Builder’s mindset with a focus on measurable impact.

Nice To Haves

Experience with payments, banking, or financial infrastructure is a strong plus.
Experience with LLMs or modern AI systems in production is a strong plus.

Responsibilities

Build AI Where It Matters Design and ship AI/ML systems embedded directly in B2B payment flows, including: Payment prioritization and acceleration Cash-flow forecasting and predictive insights Automated reconciliation, exception handling, and workflow orchestration Balance accuracy, latency, explainability, reliability, and cost in business-critical systems. Own model behavior in real-world production environments, not just offline metrics.
Multiply the Organization with AI Partner with Product, Engineering, Operations, and Finance to: Automate internal workflows using ML and LLMs Replace manual reviews and heuristics with intelligent systems Reduce cost-to-serve while increasing throughput and quality Build AI tools and platforms that allow small teams to operate at scale.
Technical Leadership & Ownership Own the end-to-end lifecycle of AI systems: problem framing, architecture, data and feature design, deployment, monitoring, and continuous improvement. Define architectural direction for AI-enabled platforms and workflows spanning multiple teams and domains. Act as a senior technical leader and force multiplier, providing clarity, judgment, and direction across concurrent initiatives. Evaluate and adopt AI, data, and automation technologies where they deliver measurable business value. Influence execution through technical leadership rather than formal authority.
Applied AI, ML Ops & Architecture Build production-grade AI systems embedded in business-critical operational workflows (e.g., payments, risk review, support triage). Design decision systems combining rules, ML inference, and self-healing capabilities. Operate and evolve ML infrastructure including: Model serving and inference pipelines Feature engineering and online/offline consistency Monitoring for data quality, model performance, and system health Work with modern cloud-native architectures: event-driven systems, streaming pipelines, and real-time processing. Make informed build-vs-buy decisions for AI and data platforms.
Trust, Reliability & Fintech Rigor Design AI systems that meet the demands of regulated financial environments. Ensure security, privacy, auditability, and explainability of AI-driven decisions. Implement safe deployment practices such as shadow mode, canary releases, back testing, and rollback. Proactively identify and mitigate risks related to bias, failure modes, and unintended system behavior.

Stand Out From the Crowd

Upload your resume and get instant feedback on how well it matches this job.

Upload and Match Resume