- Formal training or certification in Software Engineering and 5+ years of
applied experience building production systems.
- Strong system design, application development, and operational stability
skills in production environments (including on-call/incident ownership).
- Expertise designing and building microservices and RESTful APIs, with strong
knowledge of data technologies and integration patterns.
- Hands-on experience building cloud-native systems on at least one major cloud
platform (AWS, GCP, or Azure).
- Experience with distributed systems concepts (scalability, resiliency,
concurrency, idempotency, failure modes, and performance tuning).
- Proven ability to influence product design and technical direction by
proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
- Hands-on experience using enterprise-authorized AI-assisted software
development tools within the work environment, with demonstrated ability to evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
- Demonstrated experience leading effective team use of approved AI-assisted
development practices (coding, code review, test acceleration, troubleshooting), including setting expectations and reusable validation patterns.
- Hands-on experience with Large Language Models (LLMs) and generative AI use
cases (e.g., RAG, summarization/extraction, workflow assistants), including evaluation and monitoring considerations.
- Understanding of responsible AI use in engineering workflows (data
sensitivity, safe handling of inputs/outputs, resiliency/security expectations) and ability to guide peers on safe and effective usage.
- Strong communication and stakeholder management skills; ability to deliver
across multiple initiatives and drive outcomes through ambiguity.