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About This Role
About Nuuvia:
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Nuuvia, a Tyfone group subsidiary, is the leading provider of intelligent lifecycle banking solutions for community banks and credit unions. Designed to easily integrate with a financial institution’s existing digital banking infrastructure, Nuuviaʼs platform helps institutions acquire, retain, and more effectively engage account holders across multiple generations with personalized digital experiences as a Nuuvia\-branded service, a co\-branded service, or under the institution’s white\-label brand.
Nuuviaʼs flagship digital youth banking module is live with dozens of community banks and credit unions across the country, enabling them to acquire, grow, and retain youth accounts and deposits, promote financial health, and build long\-term loyalty in an increasingly competitive marketplace.
Nuuvia is an equal opportunity employer. We encourage candidates from diverse backgrounds to apply.
About the Role:
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Reporting to the Director of Engineering, we are seeking an AI Forward Engineer to embed AI deeply into how Nuuvia builds, operates, and serves 40\+ community financial institutions. This is a builder\-meets\-deployer role: half of your time will be designing and shipping production AI systems, the other half will be embedded with our credit union partners, translating their workflows and pain points into reliable AI\-powered solutions.
You sit at the intersection of applied AI, regulated\-industry compliance, and customer success. You will own AI features end\-to\-end — from prompt design and model selection through deployment, monitoring, and field iteration with real members and operators. This is not a research role; we ship to production environments serving real member financial data, in a regulated industry where compliance is a design constraint, not an afterthought.
You will work cross\-functionally with engineering, design, sales, customer success, and executive leadership to build a category\-defining solution for community financial institutions.
Responsibilities include (but are not limited to) the following:
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### Forward Deployment \& Client AI Solutions
- Embed with credit union partners to identify high\-value AI use cases — youth onboarding, financial coaching, support deflection, fraud signal detection, internal ops automation.
- Translate field requirements into production AI features that ship to that FI within weeks, not quarters.
- Customize AI workflows per institution while preserving a reusable core balance with generality.
- Run AI\-enablement sessions with FI leadership, ops, and member service teams.
- Operate as the technical face of Nuuvia AI to credit unions: requirements gathering, demos, joint design reviews, and post\-launch iteration.
### AI Systems \& Model Engineering
- Own model selection: match the right model to each task based on capability, cost, latency, privacy posture, and regulatory risk.
- Build agent and RAG pipelines using frameworks such as LangChain, LlamaIndex, Semantic Kernel, Microsoft Promptflow, or in\-house equivalents.
- Implement prompt engineering, function calling, tool use, and multi\-step agent patterns hardened for production reliability.
- Maintain a model registry — track which models are in use, for what purpose, which version, and last evaluation date.
- Monitor for model drift, hallucination rates, and output degradation; drive measurable cost efficiency through token budgeting, caching, batching, prompt compression, and smart model routing.
### AI Guardrails \& Responsible Use
- Design and implement guardrails for every AI\-assisted workflow: prompt injection prevention, PII detection and masking, output filtering, and content safety.
- Build audit trails and logging for all AI interactions — every prompt, every response, every action taken — to support regulatory examination.
- Implement human\-in\-the\-loop controls for AI\-assisted decisions with regulatory exposure (member\-facing content, account actions, eligibility logic).
- Apply industry\-standard LLM security frameworks as a baseline across all AI tooling.
- Ensure no member PII flows through external model APIs without explicit anonymization or approval.
### Regulated\-Industry Awareness
- Operate with full awareness that Nuuvia serves federally regulated financial institutions — compliance is a design constraint, not a blocker.
- Partner with internal compliance and external regulators to ensure AI\-generated content and AI\-assisted decisions meet documentation, explainability, and audit requirements.
- Ensure AI\-generated content reaching members or affecting account decisions can be explained in plain language.
- Document AI model behavior, known limitations, and risk mitigations to a standard appropriate for regulated examination.
- Treat audit readiness as a continuous practice — automated evidence collection, control testing, and policy enforcement around AI systems.
### AI Agent \& Automation Infrastructure
- Extend NautBot — Nuuvia’s internal AI agent that automates engineering ops, monitoring sweeps, ticket triage, and routine workflows across Microsoft Teams, Jira, Datadog, and Azure — and expand its coverage to additional client\-facing surfaces*.*
- Build reliable, observable automation pipelines with full traceability.
- Drive Chat Action Center automation for day\-to\-day workflows and tasks.
- Automate internal and client\-facing workflows — Jira blocked\-ticket detection, sprint automation, story\-point estimation, escalation routing, deployment alerts, incident summaries.
- Ensure all automated client\-facing messages are accurate, auditable, and contextually appropriate.
### Field Feedback Loop
- Treat every deployed AI feature as an evolving system: instrument feedback, watch real usage, and rapidly iterate.
- Translate field signal into product priorities: what worked, what failed, what regulators flagged, what FIs asked for.
- Partner with Engineering, Product, Implementation, and CSM teams to keep client\-specific work from forking the core platform.
What success looks like:
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### Within 12–18 months, success includes:
- Two\-to\-three AI features shipped to production across multiple credit unions with measurable adoption and ROI;
- A hardened guardrails/audit layer that holds up under regulated\-industry examination;
- NautBot mature enough to replace at least one manual ops workflow per quarter;
- A repeatable forward\-deployment playbook that lets the team onboard new FIs to AI features predictably;
- Demonstrable cost discipline per\-token, per\-feature, and per\-FI.
Skills and Qualifications:
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- Bachelor’s degree in computer science, Engineering, or related field (Master’s a plus).
- 4–7 years of software engineering experience, with at least 2 years shipping AI/LLM\-powered systems to production.
- Strong Python (primary) and/or TypeScript. Comfortable across the full stack when needed.
- Hands\-on experience with LLM APIs — prompt engineering, function calling, tool use, agent patterns.
- Demonstrated experience building guardrails or safety systems for AI: PII masking, output filtering, audit logging.
- Practical understanding of model risk management — documentation, validation, monitoring.
- Experience with at least one relevant framework: LangChain, LlamaIndex, Semantic Kernel, Guardrails AI, or Microsoft Promptflow.
- Solid security fundamentals: OAuth 2\.0, secret management, least\-privilege API access.
- Experience with Azure (App Services, Azure OpenAI, Key Vault, Monitor) or equivalent cloud platform.
- Comfort working in a regulated industry — or proven ability to learn fast and partner with compliance teams to ensure AI systems meet regulatory expectations.
- Comfort working in a small, senior team with minimal layers — no project managers, no ticket groomers, no handholding.
- Customer\-facing maturity: can run a meeting with a credit union CIO, COO, or fraud officer and walk out with aligned next steps.
### Strong Preference
- Forward Deployed Engineer (FDE) background — Palantir, Sierra, Glean, Anthropic Solutions, OpenAI Forward Deployed, Brex Forward Deployed, or equivalent customer\-embedded AI engineering role.
- Experience with Azure OpenAI Service and Azure AI content filtering/safety features.
- Familiarity with model evaluation frameworks (LangSmith, PromptFlow Evals, custom eval pipelines).
- Experience with PII detection and masking tools (Microsoft Presidio, AWS Comprehend, or similar).
- Prior experience in a regulated industry (financial services, healthcare, government).
- RAG patterns \+ vector search (Azure AI Search, Pinecone, pgvector).
- Microsoft Teams bot / connector development.
- Experience with general\-purpose agent harnesses (OpenClaw, Hermes, or equivalent).
- Fine\-tuning experience (LoRA/PEFT) — nice to have, not required.
### Success Profile
- A builder: ships production AI systems, not slide decks. Treats every demo as a working artifact.
- Forward\-deployed: comfortable in a customer’s environment — listening, mapping workflows, designing for their reality, not ours.
- AI\-forward: inserts intelligence into every customer and operational journey while respecting regulatory boundaries.
- Compliance\-aware: treats regulated\-industry requirements as design constraints, not blockers.
- Execution\-driven: delivers secure, scalable, observable systems with predictability.
- Cost\-disciplined: actively manages token, model, and infra spend per feature and per FI.
- A communicator: translates technical AI concepts to executives, ops teams, and engineers alike.
- A teammate: partners cross\-functionally with Engineering, Product, Implementation, CSM, Compliance, and FI counterparts.
- Customer\-obsessed: cares about whether the FI’s members are actually better off — not just shipping volume.
### What We Are NOT Looking For
- Someone who needs a research environment — we ship production systems on regulated data.
- A prompt engineer with no engineering depth — you need to own the full stack.
- Someone who treats compliance as a blocker rather than a design constraint.
- Pure backend with no tolerance for customer interaction — forward deployment is half the job.
### Benefits
- Competitive salary and bonus structure
- Comprehensive benefits package including health, dental, and 401(k)
- Dynamic work environment with passionate, driven colleagues
- Opportunity to shape the future of digital banking and payments on a global scale.
*To apply, email* *[email protected]* *with the job title as the subject line.*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Tyfone, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Tyfone AI Hiring
Tyfone has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Portland, OR, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.
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