Staff AI Engineer

Education:

Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field, or equivalent experience.

Certification/License:

None required.

Work Experience:

5+ years building and operating production AI/ML or software systems, including hands-on experience with large language models (LLMs) and/or AI agent systems.

Knowledge, Skills & Abilities:

Strong software engineering fundamentals, including proficiency in Python and experience with distributed data pipelines and CI/CD.

Demonstrated experience building and/or operating evaluation, testing, or observability capabilities for ML/AI systems in production.

Experience with LLM-based system design, including techniques such as LLM-as-judge evaluation, golden-dataset curation, and regression/drift detection.

Experience instrumenting or operating complex, multi-step systems (e.g., multi-step reasoning, tool use, retrieval, multi-agent handoffs).

Experience assessing agentic or AI systems for security, reliability, or failure modes (e.g., red-teaming or similar practices).

Ability to translate ambiguous product or technical goals into measurable, defensible engineering plans.

Strong cross-functional collaboration and communication skills; able to represent technical trade-offs to non-technical stakeholders.

Experience with cloud AI/ML platforms (e.g., AWS Bedrock) and agent frameworks (e.g., LangGraph, Strands, or similar).

Experience with cloud-native MLOps/LLMOps practices: packaging, CI/CD, A/B testing, and model or prompt versioning.

Hands-on experience with evaluation or observability platforms (e.g., Langfuse, Braintrust, LangSmith, or similar).

Track record of standing up a new technical capability (e.g., an evaluation or observability function) as a reusable, cross-team resource.

Experience using synthetic data to increase test coverage for AI/ML systems.

Experience across multiple product domains or business contexts