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About This Role
Nymbus (https://nymbus.com/) isn't just a leader in fintech; we're a community of innovators passionate about reimagining banking. Our award\-winning modern core platform and cloud\-based technology serve as the backbone for financial institutions eager to modernize and excel.
Nymbus is building the next generation of fintech platforms, and that requires a new kind of product architect. This role is the technical backbone across product squads who ensures what gets built can run safely, securely, and at scale. You will define and enforce design patterns, build foundational technical components, harden solutions for production, and own operational architecture using AI\-first methodology, working together with Builders and Shippers to deliver outcomes end\-to\-end.
WORK ENVIRONMENT:
Nymbus is a remote\-first company, with occasional travel required for team collaboration or client engagement. Working hours primarily align with the Eastern Time Zone; candidates located in the Eastern or Central time zones are preferred.
POSITION SUMMARY:
At Nymbus, the AI Product Architect is the system design and technical integrity owner in our squad triad model. You are a member of the Architecture team, report to the Chief AI Officer, and typically split your time product squads.
You are responsible for ensuring solutions meet the enterprise\-grade bar. In each squad, you work alongside a dedicated Builder and a Shipper. You own the architecture from system design through production hardening, including the technical deployment and operating model. The Shipper owns release coordination and execution, while you provide required architectural signoff for the areas you own and may hold a release until those standards are met. You operate with an AI\-first mindset, leveraging AI\-powered tools to accelerate architecture analysis, technical design, implementation, reviews, and operational workflows.
This is not a traditional architect role centered on diagrams, review boards, or governance from a distance. You are a hands\-on technical leader who regularly commits production code in the technical layers of the product, including event processing, data access, integration frameworks, observability, and shared services. You create the design and implement the foundational components that Builders use and extend to deliver product functionality. You own the "how it fits", the "how it scales", and the "how it runs safely".
THE SQUAD TRIAD MODEL
You operate as one\-third of a high\-autonomy squad:
Architect (This Role): Owns system design, technical integrity, production hardening, and cross\-squad consistency. You design and implement foundational technical components, define the technical deployment and operating model, and sign off on release readiness for the areas you own. You may hold a release when architectural, security, performance, or operational requirements have not been met.
Builder (Your Partner): Owns product intent, specification, and implementation. You are the primary creator — designing system behavior, authoring specs, and iterating with AI tools to produce working software.
Shipper (Your Partner): Owns delivery, quality assurance, release management, and operational readiness. The Shipper ensures what you build meets quality standards, passes validation, and reaches production reliably. You collaborate on acceptance criteria, test coverage, and deployment strategy.
Together, the triad owns outcomes end\-to\-end — from problem definition through production operation.
ESSENTIAL JOB FUNCTIONS/RESPONSIBILITIES:
Platform Architecture and Foundational Engineering
- Define system and reference architectures that align the solutions in each assigned squad with the broader Nymbus platform.
- Own integration patterns, API contracts, data models, data flows, tenancy boundaries, and interoperability decisions.
- Design and regularly commit production code for foundational technical components and shared services, including event processing, data access, integration frameworks, observability, and platform utilities that Builders use and extend.
- Review product specifications and implementations for scalability, maintainability, backward compatibility, and cross\-squad consistency, and define migration or coexistence patterns when needed.
- Use AI\-powered tools such as Kiro and AI services such as OpenAI and Claude to explore architecture alternatives, identify gaps, draft technical artifacts, implement foundational code, and accelerate design reviews.
Production Hardening, Testing Standards, and Non\-Functional Quality
- Define the automated testing framework and standards, including how Builder\-authored tests are implemented and how they run locally and against a Quality Assurance environment.
- Create and maintain shared test harnesses, utilities, and patterns, and establish measurable non\-functional requirements for performance, scalability, reliability, security, resiliency, and operability.
- Partner with Builders as they implement automated tests and remediate findings; partner with Shippers as they validate test completion, pass status, and the appropriate level of coverage.
- Define and oversee performance, capacity, resiliency, security, and vulnerability testing for the architectural areas you own, coordinating with the Security function where appropriate.
- Identify failure modes, bottlenecks, and operational risks before release, and enforce the engineering quality bar that distinguishes enterprise\-grade software from prototype\-grade output.
Deployment, Operations, and Observability
- Define deployment patterns across development, QA, UAT, and production environments, including configuration and environment\-management standards.
- Own the technical design for rollout strategy, progressive delivery, rollback, recovery, and failure handling; the Shipper owns release coordination and deployment execution.
- Establish monitoring, alerting, structured logging, tracing, dashboards, and other observability standards, and implement shared observability components when needed.
- Drive capacity planning, technical production\-readiness reviews, and operational runbooks, and support Builders with systemic or foundational issues while the Builder retains the SLO for support items requiring technical input or software changes.
- Provide required architectural signoff for the areas you own and hold a release when technical, security, performance, resiliency, or operational requirements have not been satisfied.
Security, Data Protection, and AI Infrastructure
- Define and verify encryption in transit and at rest, authentication, authorization, role\-based access control, secrets management, and audit logging.
- Ensure tenant isolation, data segregation, privacy controls, and regulated\-data handling are designed into each solution.
- Establish token\-based authentication and authorization patterns for AI\-enabled capabilities.
- Ensure PII and sensitive data are masked or excluded from AI contexts and that AI\-driven actions are auditable.
- Partner with Security, Compliance, and risk stakeholders to translate control requirements into practical platform patterns.
Squad and Architecture Team Collaboration
- Split time across squads, actively manage competing priorities, and maintain consistent technical patterns across both.
- Partner daily with each Builder on API design, data modeling, integration patterns, implementation tradeoffs, automated testing standards, and the use of shared foundational components.
- Regularly participate in implementation through production code commits, code reviews, pairing, prototypes, reference implementations, and shared services.
- Coordinate with each Shipper on test evidence, release readiness, deployment execution, observability, rollback preparedness, and operational supportability.
- Collaborate with the Architecture team under the Chief AI Officer; surface architectural risks and dependencies early; and maintain shared context through decision records, diagrams, specifications, reference code, and operational documentation.
Domain Context (Fintech Systems)
Build and apply working knowledge in one or more domains such as:
- Core Banking including account structures, posting models, settlement processes, configuration, and data integrity.
- Digital Banking including account opening, onboarding workflows, KYC and CIP, authentication, and user experience.
- Lending including loan lifecycles, servicing logic, payments, exception handling, and integrations.
- Payments and Cards including transaction flows, authorization, clearing and settlement, resiliency, and integration patterns.
QUALIFICATIONS:
Architecture and Technical Fluency
- 5\+ years of software engineering experience, including meaningful experience serving as a software architect, solution architect, or equivalent technical design authority for enterprise\-grade production software.
- Demonstrated ability to translate architecture into working software by designing and implementing foundational components, shared services, and technical frameworks.
- Strong hands\-on engineering capability and willingness to regularly commit production code in areas such as event processing, data access, integrations, observability, security, and platform services.
- Proven ability to define and apply architecture patterns across APIs, data, integrations, testing, security, deployment, and operations.
- Strong understanding of REST APIs, event\-driven systems, data modeling, distributed\-system tradeoffs, resiliency, and integration design.
- Experience establishing non\-functional requirements, automated testing standards, observability patterns, deployment approaches, and technical release criteria.
- Ability to balance priorities across two product squads, make pragmatic tradeoffs, and collaborate closely with Builders, Shippers, Security, and other Architects.
AI\-First Mindset
- At least 6 months of hands\-on experience using AI\-powered IDEs or coding agents such as Claude Code, Kiro, Cursor, GitHub Copilot, or equivalent tools.
- Demonstrated use of AI tools for design exploration, specification, code generation, implementation, code review, analysis, or operational automation.
- Ability to create practical guardrails, context, and review checkpoints that help AI\- generated solutions meet enterprise standards.
- Willingness to experiment with tools and continuously improve AI\-augmented architecture and engineering practices.
- Comfort operating in a spec\-first, AI\-first development methodology.
Nice to Have
- Experience designing or operating enterprise multi\-tenant SaaS applications in a cloud\-hosted environment, particularly AWS.
- Experience in fintech, banking, or financial services, including core banking, digital banking, payments, cards, lending, or adjacent financial platforms.
- A track record of modernizing or decomposing legacy applications while protecting production stability.
- Hands\-on proficiency in two or more of Java, Node.js, Python, or React.
- Experience establishing and maintaining architectural standards across multiple engineering teams.
CORE TRAITS
- High Agency: Takes ownership of system\-level outcomes and drives decisions forward without waiting for heavy process. Identifies architectural gaps and closes them.
- Problem Solver: Thrives in ambiguity. Frames complex problems clearly, explores tradeoffs, and converges on practical approaches quickly.
- Architect Mindset: Optimizes for the whole platform, not a local component. Protects cross\-squad consistency while helping each squad move quickly.
- Hands\-On Leader: Builds credibility through regular production code, shared components, prototypes, reviews, and reference implementations \- not authority alone.
- Experimenter: Uses AI tools to test architectural approaches, model failure modes, and improve patterns before scaling them.
- Quality Driven: Focuses on correctness, security, resiliency, clarity, and long\-term maintainability. Treats platform patterns as durable assets.
- Scale Oriented: Designs for growing client volume, increasing complexity, multi\-squad reuse, and operational realities from the start.
- Ownership: Does not stop at design approval. Stays accountable for architectural integrity through implementation, release signoff, and production learning, while partnering with the Builder on software changes.
WHAT WE ARE EXPLICITLY NOT LOOKING FOR
- Diagram\-only architects who are removed from implementation and production outcomes.
- Review\-board gatekeepers who identify problems but do not propose and implement practical alternatives.
- Architects who define foundational designs but expect Builders to create every technical component for them.
- People who accept prototype\-grade security, scalability, testing, or operability in production systems.
- People uncomfortable balancing priorities across two squads, working hands\-on, or sharing ownership across the triad.
- Those unwilling to adopt AI\-first development and architecture practices.
- Individuals who view architecture as a documentation function rather than a hands\-on engineering responsibility.
WHY THIS ROLE MATTERS AT NYMBUS
Nymbus is building the next generation of fintech platforms and that requires architects who can preserve technical integrity while AI\-first squads move at unprecedented speed. The AI Product Architect is the technical backbone across two squads: the person who turns working product code into secure, scalable, observable, and supportable platform capabilities, while building foundational services that allow Builders to deliver faster and Shippers to launch with confidence.
This role sits at the center of our transformation, where AI\-accelerated development, hands\-on enterprise architecture, shared platform engineering, and production operations converge to redefine how financial software is designed and run.
SALARY \& BENEFITS:
- Annual Cash Bonus and Equity Options commensurate with the role level and experience
- 100% Remote
- 401(k) plan
- Insurance \- Health, Dental and Vision
- Paid Time Off
Ready to join? We invite you to watch this video and learn who we are and how we build and innovates together!
Let's Go!
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 Nymbus, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
Nymbus, Inc. AI Hiring
Nymbus, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Jacksonville, FL, US.
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
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