AI Principal Engineer

What matters most

• Deliver production-grade AI solutions

  Lead the design, development and deployment of LLM applications, RAG pipelines and model-driven services that produce business outcomes.

• Own architecture and platform direction

  Set technical direction for major components or domains, including decisions on model selection, integrations, API design and scalability.

• Make AI reliable in production

  Establish practices for evaluation, validation, testing, monitoring and operation while maintaining performance and maintainability.

• Build in governance and security

  Ensure solutions meet enterprise security, privacy, governance and responsible AI standards in a highly regulated environment.

• Lead across teams and initiatives

  Translate requirements with product, business and data stakeholders; manage technical risks and dependencies; mentor engineers and improve engineering practices.

Likely challenges to think through

• Balancing AI quality with production constraints

  A likely challenge is choosing an LLM or RAG design that performs well without sacrificing scalability, reliability or maintainability. Prepare to explain how you would compare design options, evaluate outputs and monitor a deployed service.

• Moving quickly within governance boundaries

  Delivery may require trade-offs between iteration speed and security, privacy and responsible AI standards. Prepare an example approach for identifying risks early and working with governance stakeholders on mitigations; the posting does not specify an approval process.

• Keeping multiple initiatives technically coherent

  Owning major components while influencing several teams may make local delivery goals compete with shared platform standards. Prepare to discuss how you would resolve dependencies, communicate architectural decisions and mentor teams without assuming direct management authority.

Topics to prepare

• Production AI architecture and model selection

  Revision exercise: compare two hypothetical designs for an LLM application, covering model choice, integrations, API boundaries and scalability trade-offs. The role explicitly owns technical direction and contributes to these architectural decisions.

• Model lifecycle and production reliability

  Revision exercise: map a model from validation through deployment, monitoring, maintenance and rollback; identify failure signals and mitigations at each stage. The posting requires deep understanding of MLOps and production reliability.

• Evaluation and validation strategy

  Revision exercise: define test cases, quality measures and release criteria for a hypothetical model-driven service, including how results would be monitored after launch. Establishing evaluation, validation and testing practices is an explicit responsibility.

• Security, privacy and responsible AI

  Revision exercise: review a hypothetical AI architecture for data-exposure, governance and model-risk concerns, then explain proposed controls and unresolved trade-offs. The role must meet enterprise security, privacy, governance and responsible AI standards; model risk management is preferred knowledge.

• RAG and enterprise-scale trade-offs

  Revision exercise: diagram retrieval, generation and data-pipeline stages, then explain where retrieval quality, latency and access controls could fail. The role explicitly includes RAG pipelines; vector databases, orchestration frameworks and distributed architectures appear as preferred experience.

• Multi-team technical leadership

  Revision exercise: outline how to resolve a hypothetical disagreement between product, engineering, data and governance stakeholders, documenting the decision, risks and mentoring approach. The posting calls for cross-functional leadership across multiple initiatives and guidance of engineers.

Skills to revise

• Prototype an AI service in a listed language

  Suggested exercise: use Python, TypeScript or Rust to build a small model-driven API with input checks, error handling and tests; explain its integration boundaries. Strong proficiency in languages such as these, API design and production AI delivery are stated in the posting.

• Build and inspect a small RAG pipeline

  Suggested exercise: connect sample documents to retrieval and an LLM response, then test missing, irrelevant and restricted-source results. This practices the advertised RAG delivery work without assuming a particular vendor or framework.

• Create an operational evaluation plan

  Suggested exercise: write a compact test set and a monitoring checklist for a hypothetical deployed model, specifying what would trigger investigation or rollback. The role establishes model testing and monitoring practices and maintains models in production.

• Present an architecture decision

  Suggested exercise: prepare a one-page decision record comparing two AI solution approaches, with scalability, reliability, security, dependencies and mitigation plans; rehearse explaining it to technical and non-technical stakeholders. These are explicit principal-engineer responsibilities.

• Map evidence to the stated qualifications

  Create a one-page evidence sheet for the required degree, production AI/ML delivery and operations, a language such as Python, TypeScript or Rust, LLM frameworks, data pipelines and MLOps. Note gaps without inventing experience.

• Prepare a production AI project walkthrough

  The role calls for production-grade AI with business impact. If you have a relevant example, outline the business requirement, your technical ownership, architecture, deployment, operating decisions and measured outcome.

• Practice a scalable AI design exercise

  Sketch a hypothetical LLM application or RAG service, explaining model selection, retrieval and data flow, integrations, API design and scaling trade-offs. RAG and vector databases appear in the posting, with vector databases listed as preferred experience.

• Draft an evaluation and operations plan

  Prepare a sample plan covering model evaluation, validation, testing, monitoring, lifecycle changes and production reliability. Include how you would identify a failure, mitigate it and maintain the service.

• Work through governance and security trade-offs

  For the hypothetical design, identify privacy, security, governance and responsible-AI risks and propose controls and mitigations. Treat these as discussion proposals, not claims about Voya’s internal standards.

• Document technical leadership examples

  If available, prepare examples showing how you led a complex initiative, resolved dependencies, worked with product, engineering, data and governance stakeholders, or mentored engineers. State your decisions and their outcomes.

• Write questions for the employer

  Ask which platform components this IC Director role would own, how teams evaluate and operate AI in production, how governance partners participate in decisions, and what outcomes matter across initiatives.