AI Forward Engineer Full Time NEW

Title: AI Forward Engineer

No of Position: 1

Location: Remote, USA

Budget: $120 K/Year

About Nuuvia

Nuuvia 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.

The Role

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.

Key Responsibilities

What You Will Own

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

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.

Minimum Qualifications

• 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.