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AI Solutions Engineer II

United States·FairfaxRegular Full-Timemid

OtherAi Solutions Engineer

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Quick Summary

Overview

At Apple Federal Credit Union, we’re more than a financial institution; we’re a community-focused organization powered by passionate people. With 24 branches across Northern Virginia and a proud legacy of service, we’re committed to improving the lives of our members and the communities we serve.

Technical Tools

azurecsharppythonsqlci-cdcode-reviewcybersecuritydata-analysismentoringnetworkingperformance-optimizationproject-management

We believe our employees are our greatest asset. That’s why we foster a supportive workplace culture that values inclusiveness, innovation and growth. Whether you’re just starting out or advancing your career, you’ll find opportunities for professional development, mentorship and meaningful impact.

What We Offer

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✓Recognized as a USA Top Workplace (2025) and Top Workplace by The Washington Post (2025)

✓Collaborative, welcoming environment with forward-thinking leadership

✓Competitive, comprehensive benefits package, including:Medical, dental and vision coverage

✓401(k) with employer match

✓Paid time off and 11 paid federal holidays

✓Paid volunteer time to give back

✓Tuition reimbursement and ongoing training opportunities

✓Annual TEAM Bonus plan.

Responsibilities

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Design and implement staff-facing AI experiences that allow non-technical users to ask questions in natural language (e.g., “How many members opened accounts yesterday?”) and receive accurate, explainable answers grounded in governed enterprise data.
Implement safe “natural language → analytics” workflows (query planning, schema/metadata grounding, semantic definitions, and response formatting) that prioritize correctness, interpretability, and repeatability.
Ensure AI outputs include the appropriate context (definitions, time windows, filters used, and confidence/limitations) to reduce misinterpretation and improve trust.
Partner with Data Engineering to assess and improve AI-readiness of enterprise datasets, develop metadata, semantic labeling, and data quality standards needed for AI outputs.

Own the technical evaluation, selection, and implementation of AI tooling used to deliver internal AI solutions (model/provider choices, orchestration patterns, retrieval patterns, evaluation harnesses, monitoring/telemetry patterns).
Evaluate and develop agentic AI solutions that will assist in everyday organizational processes, build out multi-step workflows that will act on enterprise data with appropriate oversight and traceability.
Develop reusable “golden path” components that the team can consistently apply: prompt templates, retrieval patterns, tool/function calling patterns, safety/guardrail patterns, logging and tracing, and secure data access adapters.

Partner with Data Engineering to define and maintain the interfaces between the AI consumption layer and the governed data platform (Fabric/Warehouse/Lakehouse), ensuring minimal friction for future AI use cases.

Architect and implement the runtime environment for AI services (Azure hosting patterns, identity, secrets, compute scaling, performance tuning, cost controls) to ensure production-grade reliability and supportability.
Implement observability and operational discipline for AI solutions: telemetry, auditing, error handling, incident response hooks, and maintainable runbooks.
Establish release and change discipline for AI solutions (dev/test/prod separation where applicable, automated deployment patterns, rollback approach).

Ensure AI solutions comply with Apple FCU’s AI Policy and enterprise security expectations, including data access controls, privacy safeguards, auditability, and human oversight mechanisms.

Partner with stakeholders (Cyber/IT governance) to build out AI governance framework and ensure AI tools do not introduce unacceptable data exposure risk; support third‑party AI assessments as needed (data usage boundaries, retention, model training exclusions, access logging).

Create practical enablement materials and lightweight training for staff on how to use AI data agents effectively and responsibly (what it can/can’t answer, how to phrase questions, how to validate outputs).

Operate as a “builder + evangelist”: identify adoption blockers, iterate on UX and response quality, and drive measurable increases in self-service usage while reducing manual analyst/engineer lift.

Experience:

5–8+ years of demonstrated, relevant experience in software engineering, data engineering, platform engineering, and/or applied AI engineering.
Strong proficiency in Python (and/or C#) and modern engineering practices (Git, code reviews, testing, CI/CD, observability).
Working knowledge of Azure and cloud-native design patterns (secure identity, networking, secrets management, scaling/compute, cost awareness).
Strong working knowledge of SQL and data warehousing/lakehouse concepts; ability to safely operationalize natural-language analytics patterns using governed datasets and business definitions.
Demonstrated ability to evaluate tools pragmatically (build vs. buy), select architectures, and ship production solutions in ambiguous environments (“think and do”).
Demonstrated experience with RAG patterns, vector search, prompt engineering, and LLM output evaluation techniques
Familiarity with emerging agentic AI platforms and runtimes, with the ability to evaluate and explore available tools that support persistent agent execution, governance, and security
Demonstrated understanding of responsible AI expectations (human oversight, privacy, security, transparency, and risk management) and ability to implement guardrails accordingly.

BA/BS degree with an emphasis in IT/IS, Computer Science or equivalent combination of experience and relevant certifications

Must possess excellent verbal and written communication skills. The ability to motivate or influence others is a material part of the job, requiring a significant level of diplomacy and trust. Obtaining cooperation (internally and/or externally) is an important part of the job. Must be able to communicate complex AI concepts to diverse audiences, ensuring clarity and understanding across all levels of the organization. Collaborates effectively with cross-functional teams to achieve AI objectives, exemplifying leadership and team-building abilities.

Strong analytical, prioritizing, interpersonal, problem-solving, presentation, allocating, project management (from conception to completion), & planning skills.

Strong verbal and written communication skills (including analysis, interpretation, & reasoning).

Skilled at understanding, detailing, and describing complex technical subjects, and aligning business capabilities to Technology solutions.

Ability to develop and maintain collaborative relationships with peers and colleagues across the organization, as well as internal and external clients.

Ability to work well autonomously and within a team in a fast-paced and deadline-oriented environment.

Apple Federal Credit Union values, encourages, and implements diversity in the workplace.

As an equal opportunity employer, Apple Federal Credit Union does not discriminate in employment with regard to race, color, religion, national origin, citizenship status, ancestry, age, sex (including sexual harassment), sexual orientation, marital status, physical or mental disability, military status or unfavorable discharge from military service or any other characteristic protected by law.

All selected candidates will be subject to credit and background checks to determine employment eligibility.