This role is HYBRID in Fairfax, VA requiring you to be in the office Monday, Tuesday and Thursday. Role:Under the general supervision of the Manager, AI & Data Engineering, and in adherence to established policies and procedures, the AI Solutions Engineer II will be responsible for designing, building, and operationalizing AI-powered solutions that transform how staff discover and consume Apple FCU's enterprise data. This role will serve as a hands-on technical builder who leverages the data platform and curated datasets created by the Data Analytics team to deliver secure, reliable, and scalable AI experiences-such as internal agents that enable staff to ask natural-language questions and receive accurate, explainable answers grounded in governed data.
The AI Solutions Engineer will own key technical decisions and implementations required to make AI usable in production, including AI tooling selection and development, integration patterns, Azure compute/runtime considerations, and end-to-end operational supportability (monitoring, logging, error handling, and deployment discipline). The role will partner closely with Data Engineering, Data Analytics leadership, Cybersecurity/IT stakeholders, and business partners to ensure solutions meet business needs while maintaining appropriate governance, privacy, and security controls.
The candidate will be expected to perform their duties with a mindset that reflects The Apple Way principles: Team Up, Serve with Purpose, Challenge Yourself, and Own It. A keen awareness of and compliance with credit union policies and procedures, as well as regulations pertaining to the Bank Secrecy Act, is imperative. Additionally, the AI Solutions Engineer will undertake other information technology responsibilities as delegated by the Manager, AI & Data Engineering.
Essential Functions & Responsibilities:Build AI-powered data consumption experiences- 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 12 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 AI tooling selection, engineering standards, and integrations- 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.
Azure runtime, compute, and operational ownership- 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).
Governance, security, and responsible AI guardrails- 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).
Enablement and adoption (turn capability into usage)- 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.
Education:- 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.
